dissolved gas analysis for fault diagnosis of power transformers sukhbir singh

I

DISSOLVED GAS ANALYSIS FOR FAULT

DIAGNOSIS OF POWER TRANSFORMERS

A thesis

Submitted

For the award of the Degree of

DOCTOR OF PHILOSOPHY

By

SUKHBIR SINGH

Electrical Engineering Department

National Institute of Technology Kurukshetra

Kurukshetra, India

Registration No.: Ph. D. -1127E/2006

2012

II

DISSOLVED GAS ANALYSIS FOR FAULT

DIAGNOSIS OF POWER TRANSFORMERS

A thesis

Submitted

For the award of the Degree of

DOCTOR OF PHILOSOPHY

By

Registration No.: Ph. D. -1167E/2006

SUKHBIR SINGH

Under the supervision of

Dr. DHEERAJ JOSHI

Department of Electrical Engineering

National Institute of Technology Kurukshetra

Kurukshetra, India

2012

I

DECLARATION

I hereby declare that;

i) The present work in thesis is my own and it has been carried out under

the able guidance of my supervisor.

ii) The work has not been submitted to any other institute/university for

the award of degree or diploma.

iii) I have adhered to the institute guidelines/regulations while preparing

the contents of the thesis.

iv) Wherever I have used material (reports, data, book, journal, text, and

figure) from other source, I have given due credits by citing in the text

of the thesis with the details in the references.

Date: .2012 (SUKHBIR SINGH)

Registration No.: Ph. D.-1167E/2006

II

CERTIFICATE

Certified that the thesis entitled, “DISSOLVED GAS ANALYSIS FOR

FAULT DIAGNOSIS OF POWER TRANSFORMERS”, submitted by

Mr. SUKHBIR SINGH is in fulfillment of the requirements for the award

of the degree of DOCTOR OF PHILOSOPHY in ELECTRICAL

ENGINEERING from NATIONAL INSTITUTE OF TECHNOLOGY,

KURUKSHETRA. The candidate has worked under my supervision. The

work presented in this thesis has not been submitted for the award of any

other degree/diploma.

(Dr. DHEERAJ JOSHI)

Assistant Professor

Electrical Engineering Department

National Institute of Technology

Kurukshetra-136119, India

III

ACKNOWLDGEMENTS

Like all faithful researchers, it is my prime duty to acknowledge all the books, research

journal, periodicals and other sources of knowledge from whom, I have sought

knowledge and inspiration for the completion of this present stupendous research work.

I am beholden beyond words in gratitude to Dr. Dheeraj Joshi, Assistant Professor, EED,

NIT, Kurekshetra for his valuable guidance and cooperation to complete this thesis work.

I am also grateful to Dr. M. N. Bandyopadhyay, Director, NIT, Calicut, erstwhile

Director, NIT, Kurukshetra for initiating me to this area of research.

For extending critical counsels and periodic encouragement, my heartfelt thanks are due

to Prof. Akhilesh Swarup and Prof. K. S. Sandhu, EED, NIT, Kurukshetra. I am highly

thankful to Prof. (Mrs.) L. Deewan, HOD, EED, NIT, Kurukshetra for facilitating the

process of my research work at various stages.

I am sincerely grateful to my children Er. Monika Bainsala, Er. Sukhanshu Singh and

Master Kaushal Singh, who always have been constant inspiration to continue my

research work even during the movements of my drudgery, tiresomeness and nervous

break-down. For my dear wife, I extend my sincere gratitude for her untiring patience and

unlimited fore bearance without any complaint.

Special thanks to M. Tech. students of NIT Kurukshetra, namely Er. Mithlesh Verma, Er.

Kavita Jaiswal, Er. Reshmi PR. for rendering a number of jobs during the course of

pursuit of this research work for the last one year.

For providing precious learning, testing, materials and reports, etc for this thesis work, I

am thankful to Messers R & D NTPC HQ. NOIDA, CPRI Testing Laboratory NOIDA

and TIFAC CORE, NIT Hamirpur.

I also acknowledge help of those, whom I could not inadvertently mention here, but who

contributed to the completion of this work directly or indirectly.

To all my critics and debunkers, I also thank for giving courage and strength to tolerate

their bitter criticism.

Last but not the least, I acknowledge the help and encouragement given to me by Prof. S.

D. Sharma, former Professor & Dean Dept. of English, G B Pant Nagar University, Pant

Nagar for bringing about remarkable language competence.

In the end I bow my head in reverence to ALMIGHTY GOD, who has given me such a

fine life to live by.

(SUKHBIR SINGH)

IV

ABSTRACT

Power transformers are designed to distribute and transmit electrical power. In case of

failure, replacement cost of major power transformer becomes very high in the power

industry. Catastrophic failure not only results in unavailability of equipment and revenue

loss, but also the damage of peripheral equipment, environmental damage, and alternative

power resources. There could be two causes, include external factors such as short circuit,

overloading of system lightning strikes, system switching operations and internal factors

such as overheating, insulation deterioration, loss of winding clamping, presence of solid

contaminations, moisture, oxygen, and winding resonance.

Various off-line and on-line oil tests for fault diagnoses of power transformers are

conducted periodically as per expert recommendation. A number of standards have

evolved over the time on transformer loading and power transformer fault diagnosis to

minimize unplanned outages. Among all the existing diagnostic techniques, dissolved gas

analysis (DGA) is an important technique to assess incipient fault in a power transformer

by analyzing ratios or/and contents of specific dissolved gas concentrations arising from

the deterioration of transformer liquid/solid insulations.

DGA interpretations use methods such as key gases, Roger‘s ratio, Dornenburg‘s ratio,

CIGRE‘s standards, IEC/IEEE standards, Duval triangle, etc. Still, there is no universally

accepted interpretation technique for DGA. Further its acceptance becomes need based

depending upon economy, environment or geographical conditions of a continent,

country, authority, organization, utility, etc. An expert opinion is always sought whenever

any damaging trend is occurring as a result of DGA fault diagnostic techniques.

Dissolved gas analysis for fault diagnosis of power transformers is done periodically

including other tests as per the expert‘s recommendation. DGA analysis shows the

distribution of key gases dissolved in oil. Generation of these key gases are due to thermal

and electrical faults, and provide an indication of type of faults, presence and severity of

the faults in the power transformer, However, sometimes other offline or invasive tests

such as sweep frequency response analysis, thermography, UV-tests, furan analysis,

degree of polymerization are required to confirm the exact fault. A huge amount of cost is

involved in fault diagnostic. Therefore, it becomes equally important to use better

diagnostic method or a combination of methods to detect probable failure and its root

V

causes. So fault diagnostic capability also includes a list of faults. Preparation for

necessary corrective action can be planned in advance and executed quickly.

Several methods for DGA interpretations in power transformers in service are available in

IEC Standard 60599, the IEEE Guide C-57-104 as well as in published reviews on the

topic. The Duval triangle method is also available in IEEE standards. However, usesr are

not familiar or not quite at ease with the use of triangular graphical analysis. An effort has

been made in this thesis work to simulate Duval triangle method 1 to Duval triangle

method 5 for dissolved gas analysis for fault diagnosis of power transformers. Duval

triangle1 method uses three hydrocarbon gases only (methane, ethylene and acetylene).

These gases are decomposition of transformer oil corresponding to the increasing levels

of energy required to generate gases under thermal and electrical stresses. In addition to

six mentioned faults (PD, D1, D2, T1, T2 and T3) an intermediate zone DT is available in

Duval triangle method 1 as compared to the other methods. More than 60 fault reports

collected from various authorities and DGA test laboratories, which are analyzed for fault

interpretations by graphical triangular coordinates, calculated manually and the same are

cross verified with existing faults.

All the faults reports were simulated in Duval triangle method 1. These are again cross

verified with existing faults and manual faults analysis with simulated faults analysis. It

revealed that DGA fault analysis in power transformer with Duval triangle methods

provide better overall diagnosis results at ease with improved efficiency and accuracy.

Artificial intelligence methods can be used for enhancing resolution of faults

identification by fusion on information from multiple tests and available transformer

information. Depending on the type of diagnostic technique and available knowledge, the

development of shallow models of artificial intelligence technique required are as

follows:

A. Diagnostic rules (used standard)

B. Data driven model (FUZZY,ANN)

C. Case history data (for expert opinion)

To increase the efficiency and accuracy in DGA analysis with Duval triangle method for

fault diagnosis of power transformer is achieved in this thesis work with application of

artificial intelligence techniques (Fuzzy logic and artificial neural network).

Fuzzy logic (FL) is applied for all the Duval triangle methods. In this process, all the zone

boundaries of the faults in each Duval triangle are arranged and faults are assigned at

VI

each vertex. Ranges for three input percentages and outputs are assigned to create

membership function. Fuzzy rules using fuzzy membership function are decided with

range of input percentages and output as fault using crisp boundaries. Fuzzy logic

approaches of Duval triangle1 faults diagnosis on reported faults showed better crisp

faults and reduced the uncertainties between the faults, and the increasing accuracy.

Artificial neural network (ANN) is also applied for all Duval triangles. Polygon

coordinates of all Duval triangles are developed to large string of input percentages for

triangular coordinates. Some test inputs are also assigned. A feed-forward, back

propagation (BP) ANN for this thesis work has been assigned with input and output

patterns. For ANN configuration and training, the Duval triangles are applied with tan-

sigmoid (TANSIG) transfer function with Levenberg- Marquardt (TRAINLM) training.

The result of DGA fault diagnosis of power transformer shows that superior learning

capability of ANN gives quicker analysis as compared to other faults diagnosis method.

VII

CONTENTS

Page No.

DECLARATION (I)

CERTIFICATE (II)

ACKNOWLDGEMENTS (III)

ABSTRACT (V)

CONTENTS (XI)

LIST OF FIGURES (XV)

LIST OF TABLES (XX)

CHAPTER 1

INTRODUCTION 1-23

1.1 INTRODUCTION 1

1.2 TRANSFORMER 1

1.3 TYPES OF TRANSFORMERS 2

1.4 CHOICE OF VOLTAGES 5

1.5 SPECIFICATIONS WITH DESIGN PRACTICES OF POWER TRANSFORMER 6

1.5.1 Basic Objectives of Specifications 8

1.5.1.1 Specification for power transformers 9

1.5.2 Design Practices of Power Transformers 9

1.5.2.1 Basic physical technologies 11

1.5.2.2 Requirements 11

1.6 POWER LOSSES 12

1.6.1 Technical Loss 12

1.6.2 Commercial Loss 12

1.7 POWER SYSTEM RELATED LOSSES IN POWER TRANSFORMERS 13

1.7.1 Distribution Transformers (DTs) not Located at Load Center on the

Secondary Distribution System 13

1.7.2 Over-Rated Distribution Transformers and hence their Under-Utilization 14

1.8 SELECTION OF POWER TRANSFORMERS 14

1.9 POWER TRANSFORMER FAILURES AND PROBLEMS 15

1. 10 NECESSITY OF FAULT DIAGNOSIS 17

1.11 BENEFITS OF FAULT DIAGNOSIS 19

1.12 ORGANISATION OF THESIS 20

VIII

1.13AUTHOUR‘S CONTRIBUTION 22

CHAPTER 2

LITERATURE SURVEY 24-37

2.1 INTRODUCTION 24

2.2 FAILURE SURVEY ON TRANSFORMERS 24

2.3 FAULT DIAGNOSIS OF POWER TRANSFORMERS 25

2.4 TESTING OF SOLID INSULATIONS 26

2.4.1 Degree of Polymerization 27

2.4.2 Furan Contents 27

2.5 TESTING OF TRANSFORMER INSULATION OIL FOR FAULT DIAGNOSIS IN

POWER TRANSFORMERS 28

2.5.1 Break Down Voltage (BDV) 28

2.5.2 Acidity 28

2.5.3 Flash Point 29

2.5.4 Loss Factor 29

2.5.5 Viscosity 29

2.5.6 Tan-delta, Resistivity and Dielectric Constant 29

2.6 DGA FAULT DIAGNOSIS OF POWER TRANSFORMERS 30

2.6.1 On-line/Off-site DGA 30

2.6.2 DGA Duval Triangle Methods 30

2.6.3 Standards and Guides for Fault Interpretations 31

2.7 ARTIFICIAL INTELLIGENCE (AI) APPLICATION

IN DGA OF POWER TRANSFORMERS 34

2.8 ADVANCEMENTS IN FAULT DIAGNOSIS METHODS OF POWER

TRANSFORMERS 34

2.8.1 Thermography 34

2.8.2 Sweep Frequency Response Analysis (SFRA/FRA) 35

2.8.3 Thermal UV- Response Test on Transformer Oil 35

2.8.4 Fault Detection (winding deformation) by Transformer Tank Vibration 35

2.8.5 Alternative Insulating Fluids and their DGA 36

IX

CHAPTER 3

TRANSFORMER INSULATING MATERIALS AND OIL TESTING 38-57

3.1 INTRODUCTION 38

3.2 INSULATING MATERIALS 38

3.3 PROPERTIES OF INSULATING MATERIAL 38

3.4 TRANSFORMER PAPER INSULATION 39

3.5 TRANSFORMER INSULATING OIL 40

3.6 INSULATING OIL QUALITY 41

3.7 FACTORS AFFECTING INSULATING OIL QUALITY 42

3.7.1 Effect of Oxygen on Oil 42

3.7.2 Moisture in Oil 42

3.7.3 Absorption of Moisture by Insulating Materials 44

3.7.4 Absorption of Nitrogen by Oil 44

3.8 IMPORTANT OIL SPECIFICATIONS 44

3.9 TESTING OF TRANSFORMER OIL 46

3.9.1 Non Traditional Tests on Transformer Oil 48

3.9.2 Traditional Tests for Fault Diagnosis 50

3.10 IFT-NN RELATIONSHIP 51

3.11 THERMAL DEGRADATION TEST ON PURE TRANSFORMER OIL 52

3.12 THERMAL DEGRADATION TEST ON OIL WITH PRESS-BOARD AND

KRAFT PAPER 53

3.13 DEGRADATION TESTS DUE TO ACCUMULATION

OF WATER CONTENT 55

3.14 SUMMARY 57

CHAPTER 4

DISILVED GAS ANALYSIS OF POWER TRANSFORMERS 58-92

4.1 INTRODUCTION 58

4.2 DECOMPOSITION OF OIL 58

4.3 FORMATION OF GASES IN TRANSFORMER OIL 59

4.4 SOLUBILITY OF GASES IN TRANSFORMER OIL 60

4.5 DECOMPOSITION OF CELLULOSE INSULATION 61

X

4.6 OTHER SOURCES OF GASES IN TRANSFORMERS 61

4.7 DISSOLVED GAS ANALYSIS 63

4.8 FAULTS IN PAPER AND FAULTS IN TRANSFORMER OIL 64

4.9 SAMPLING OF LIQUID INSULATION AND GAS EXTRACTION FROM OIL 64

4.9.1 Sampling Procedures 64

4.9.2 Taking Gas Samples for Gas Chromatograph Analysis 65

4.10 INFLUENCE OF STORAGE OF OIL SAMPLES ON DGA 65

4.10.1 Effect of Air Bubbles 66

4.10.2 Effect of Temperature 66

4.10.3 Effect of Light 67

4.11 TAKING OIL SAMPLES FOR DGA 68

4.12 TECHNIQUES FOR SAMPLING INSULATING LIQUIDS 68

4.13 GAS EXTRACTION FROM SAMPLE 70

4.14 NEW DGA SYSTEM 71

4.15 VACUUM DEGASSING 71

4.16 MOBILE GAS CHROMATOGRAPHY 72

4.17 ONLINE DGA SYSTEM 73

4.18 GASES ANALYZED BY DGA 75

4.19 FAULTS DETECTION BY DGA 76

4.19.1 Partial discharges (PD) of the corona-type (PD) 76

4.19.2 Discharges of low energy (D1) 76

4.19.3 Discharges of high energy (D2) 76

4.19.4 Thermal faults of temperatures < 300°C (T1) 76

4.19.5 Thermal faults of temperatures between 300 °C and 700°C (T2) 77

4.19.6 Thermal faults of temperatures > 700 °C (T3) 77

4.20 STANDARDS AND CODES FOR DGA INTERPRETATIONS ADOPTED IN

INDIA 77

4.21 OTHER TRANSFORMER CHEMISTRY SERVICES

DURING INTERPRETATION 78

4.22 DIFFERENT DGA METHODS 81

4.23 KEY GASES METHOD 81

4.23.1 Evaluation of Possible Fault Type by the Key Gas Method 81

4.24 EVALUATION OF TYPES OF POSSIBLE FAULT BY ANALYSIS

OF THE SEPARATE COMBUSTIBLE GASES GENERATED 83

XI

4.25 THE DORNENBURG‘S RATIO METHOD 83

4.26 THE MODIFIED ROGER‘S RATIO METHOD 86

4.27 DUVAL TRIANGLE METHOD 87

4.28 IEC 60599 88

4.28.1 EC 60599 Ratio-Fault Diagnosis 89

4.29.2 Rates of Gas Increase 89

4.29.3 IEC 60599 Typical Gas Levels 89

4.30 NOMOGRAPH METHOD 90

4.31 DENKYOKEN METHOD 90

4.32 CIGRE‘S METHOD 91

4.33 NBR7274 METHOD 91

4.34 IS 10593:2006 METHOD 91

4.35 COMPARATIVE ANALYSIS OF DIFFERENT DGA METHODS (KEY GAS,

DORNENBURG‘S, ROGER‘S RATIO & DUVAL TRIANGLE METHOD) 92

4.36 SUMMARY 92

CHAPTER 5

FAULT DIAGNOSIS WITH DUVAL TRIANGLE 93-134

5.1 INTRODUCTION 94

5.2 FAULT DIAGNOSIS BY DUVAL TRIANGLE METHOD 94

5.3 IMPROVED VERSION OF DUVAL TRIANGLE 1 METHOD 95

5.4 THE INFLUENCE OF LABORATORY ACCURACY ON FAULT DIAGNOSIS 96

5.4.1 Influence of Factors Affecting Transformer Reliability 98

5.4.2 Calculation of Own Typical Values 98

5.4.3 Adjustment of Pre-failure and Alarm Values 99

5.5 APPLICATION OF DUVAL TRIANGLE METHOD 1 (DTM 1) 100

5.5.1 Graphical Fault Interpretation 100

5.5.2 Simulated Fault Interpretation with DTM1 101

5.6 DUVAL TRIANGLE 2 FOR LOAD TAP CHANGERS OF THE OIL TYPE 104

5.6.1 Implementation and Results from Duval Triangle 2 (DTM 2) for OLTCs 106

5.7 THE DUVAL TRIANGLES 3 FOR NON-MINERAL OILS 108

5.7.1 Implementation and results from Duval triangle methods 3 (DTMs 3) for Non-

mineral oils 110

5.7.1.1 Silicone oil 110

XII

5.7.1.2 Midel oil 112

5.7.1.3 FR 3 oil 114

5.7.1.4. Bio-Temp oil 115

5.8 DUVAL TRIANGLES 4 AND 5 FOR LOW-TEMPERATURE FAULTS IN

TRANSFORMERS 117

5.8.1 Duval Triangle 4 for Low-Energy Gases 117

5.8.2 Implementation and Results for Duval Triangle Method 4 118

5.8.3 Duval Triangle 5 (DTM5) for Low-Temperature Faults 120

5.8.4 Implementation and Results for Duval Triangles Method 5 122

5.9 SUMMARY 134

CHAPTER 6

APPLICATION OF ARTIFICIAL INTELLIGENCE IN DUVAL TRIANGLE

METHODS 135-202

6.1 INTRODUCTION 135

6.2 ARTIFICIAL INTELLIGENCE TECHNIQUES 135

6.3 FUZZY LOGIC TECHNIQUE (FLT) 135

6.4 FUZZY LOGIC APPROACH IN DTM1 138

6.5 FUZZY LOGIC APPROACH FOR DTM2 144

6.6 FUZZY LOGIC APPROACH FOR DTM 3 147

6.6.1 Fuzzy Logic Approach for DTM3, Silicone oil 147

6.6.2 Fuzzy Logic Approach for DTM3, Midel Oil 153

6.6.3 Fuzzy Logic Approach for DTM3, FR3 Oil 157

6.6.4 Fuzzy Logic Approach for DTM3, Bio-Temp Oil 162

6.7 FUZZY LIGIC APPROACH FOR DTM4, LOW ENERGY FAULTS 167

6.8 FUZZY LIGIC APPROACH FOR DTM5, LOW TEMPERATURE FAULTS 170

6.9 ARTIFICIAL NEURAL NETWORK TECHNIQUES (ANNT) 175

6.10 ANN APPROACH FOR DTM1 178



6.12.1 ANN Approach for DTM3, Silicone Oil 184

6.12.2 ANN Approach for DTM3, Midel Oil 187

6.12.3 ANN Approach for DTM3, FR3 Oil 190

6.12.4 ANN Approach for DTM3, BioTemp Oil 195

XIII



6.15 SUMMARY 202

CHAPTER 7

CONCLUSIONS AND FUTURE SCOPE OF WORK 203

7.1 CONCLSIONS 203

7.2 FUTURE SCOPE OF WORK 203

REFERENCES 204-213

APPENDIX--A 214-219

APPENDIX-B 220-243

PUBLICATIONS FROM THESIS WORK 244-245

XIV

LIST OF FIGURES

Figure 2.1: Causes of transformer failure 24

Figure 2.2: Percentage of defective components of transformers 25

Figure 2.3: Percentage of failure detection methods 25

Figure 3.1: Dielectric Strength Vs Water in Oil 43

Figure 3.2: Water Solubility Vs Temperature 43

Figure 4.1: Gas Generation Chart 60

Figure 4.2: Relative Solubility (Y-Axis) as Function of Temperature OC (X-Axis) 61

Figure 4.3: Influence of air bubbles of different volume on dga results 66

Figure 4.4: Stray gassing effect at 90 0C 67

Figure 4.5: Influence of sunlight and darkness on concentration of

H2 and CO2 on new oil 67

Figure 4.6: Influence of sunlight and darkness on concentration of

H2 and CO2 on aged oil. 68

Figure 4.7: Toepler pump for laboratory use 70

Figure 4.8: Schematic view of the gas extraction nit 71

Figure 4.9: Photo of the gas extraction unit 71

Figure 4.10: Mobile GC 73

Figure 4.11: Online DGA system 74

Figure 4.12 Key gases (Over Heating, Oil) 82

Figure 4.13: Key gases (Over Heating, Paper) 82

Figure 4.14: Key gases (Partial Discharge) 82

Figure 4.15: Key gases (Arcing) 82

Figure 4.16 : Dornenburg‘s ratio method flow chart 86

Figure 4.17: Modified Roger‘s ratio method Flow-chart 87

Figure 4.18: Duval Triangle 1 87

Figure 4.19: Duval Triangle indicating regions of different faults 88

Figure 4.20: Logarithmic nomograph 90

Figure 5.1: Duval triangle 93

Figure 5.2: Duval triangle 1 graphical plot 95

Figure 5.3: Actual cases of faults visually inspected in different transformers 96

XV

Figure 5.4: Uncertainty on diagnoses for many cases 97

Figure 5.5: Diagnosis uncertainties to laboratory analytical accuracy

of +- 15, 30, 50 and 75% PFS, in % 97

Figure 5.6: Probability of Having a Failure-Related Event(PFS, in %) as a

Function of C2H2 Concentration in Service of in ppm and Norm in % 99

Figure 5.7: Graphical analyses on DTM 1 101

Figure 5.8: Flow-chart of DTM 1 103

Figure 5.9: Duval Triangle 1 simulation analysis 104

Figure 5.10: The Duval Triangle 2 for LTCs of Oil Type Transformers 105

Figure 5.11: Inspected Cases of Faults in the Duval Triangle 2 for OLTCs of oil-type

Transformers 105

Figure 5.12: Fault analysis by DTM 2 for OLTC 108

Figure 5.13: Duval Triangle 3 for Non-mineral oils with their zone

boundaries in colors 109

Figure 5.14: Fault analysis results of DTM 3 for non-mineral oil (Silicon oil) 112

Figure 5.15: Fault analysis results by DTM3 (Midel oil) 113

Figure 5.16: Fault analysis results by DTM3 (FR3 oil) 115

Figure 5.17: Fault analysis result by DTM 3 (BioTemp oil) 117

Figure 5.18: Duval triangle 4 transformers filled with mineral oil 118

Figure 5.19: Fault analysis results by DTM 4 for low-intensity faults 120

Figure 5.20: Duval triangle 5 for transformers filled with mineral oil

(Low-Temperature Gases) 120

Figure 5.21: Fault analysis results by DTM 5 for low temperature fault 123

Figure 5.22: Generalized flow-chart for complete solution by Duval Triangles 124

Figure 6.1: Basic fuzzy logic control 136

Figure 6.2: Fuzzy logic approach in DTMs 138

Figure 6.3(a): %CH4 membership-function 139

Figure 6.3(b): %C2H4 member functions 140

Figure 6.3(c): % C2H2 member functions 140

Figure 6.4: membership function of DTM1 faults 142

Figure 6.5: FIS analysis of fuzzy rules for DTM 1 144

Figure 6.6 (a): DTM 2 Membership functions of CH4 145

Figure 6.6 (b): DTM 2 Membership functions of C2H4 145

Figure 6.6 (c): DTM 2 Membership functions of C2H2 145

XVI

Figure 6.7: OLTC‘s faults output ranges 146

Figure 6.8: FIS analysis of fuzzy rules for DTM2 147

Figure 6.9(a): Membership functions for CH4 DTM3, Silicone oil 148

Figure 6.9(b): Membership functions for C2H4 DTM3, Silicone oil 148

Figure 6.9(c): Membership function for C2H2 DTM3, Silicone oil 148

Figure 6.10: Output membership functions of silicone oil 150

Figure 6.11: FIS analysis of fuzzy rules for DTM3, Silicone oil 152

Figure 6.12 (a): Fuzzy membership function of CH4 for DTM3, Midel oil 153

Figure 6.12 (b): Fuzzy membership function of C2H4 for DTM3, Midel oil 153

Figure 6.12 (c): Fuzzy membership function of C2H2 for DTM3 Midel oil 153

Figure 6.13: Output membership functions for DTM3, Midel oil 155

Figure 6.14: FIS analysis of fuzzy rules for DTM3, Midel oil 157

Figure 6.15 (a): Fuzzy membership functions for DTM3, FR3 oil 158

Figure 6.15 (b): Fuzzy membership functions for DTM3, FR3 oil 158

Figure 6.15 (c): Fuzzy membership functions for DTM3, FR3 oil 158

Figure 6.16: Output membership functions for DTM3 FR3 oil 160

Figure 6.17: FIS analysis of fuzzy rules for DTM3, FR3 oil 162

Figure 6.18 (a): Input membership function CH4 in DTM3, BioTemp oil 163

Figure 6.18 (b): Input membership function C2H4 in DTM3, Biotemp oil 163

Figure 6.18 (c): Input membership function of C2H2 in DTM3, BioTemp oil 163

Figure 6.19: Fuzzy outputs of DTM3, BioTemp oil 165

Figure 6.20: FIS analysis of fuzzy rules for DTM3, BioTemp oil 166

Figure 6.21 (a): Fuzzy membership functions of H2 for DTM4 167

Figure 6.21 (b): Fuzzy membership functions of CH4for DTM4 168

Figure 6.21 (c): Fuzzy membership functions of C2H6 for DTM4 168


Figure 6.23 (a): Fuzzy membership functions of CH4 in DTM5 171

Figure 6.23 (b): Fuzzy membership functions of C2H4 in DTM5 171

Figure 6.23 (c): Fuzzy membership functions of C2H6 in DTM5 172

Figure 6.24: Fuzzy output membership function for DTM5 173


Figure 6.26: Two-layered feedforward neural Network 175

Figure 6.27: Two-layer BP-ANN for DTM1 180

Figure 6.28: Validation performance chart 181

XVII

Figure 6.29 (a): Mu and Gradient plot 181

Figure 6.29 (b): Target plots 181





Figure 6.33: Two-layer BP-ANN for DTM3, silicone oil 186

Figure 6.34: Validation performance chart Figure 186



Figure 6.36: Two-layer BP-ANN for DTM3, silicone oil 188




Figure 6.39: Two-layer BP-ANN for DTM3, FR3 oil 191




Figure 6.42: Two-layer BP-ANN for DTM3, BioTemp oil 194












XVIII

LIST OF TABLES

Table 1.1: Material used for Transformer Manufacturing 7

Table 1.2: General Information & Technical Specification 10

Table 1.3: Calculation of AT&C Losses 13

Table 1.4: Main causes of failure of transformer in service 15

Table 1.5: Components causing failure in service 16

Table 1.6: Typical causes of transformer failures 16

Table 3.1: Electric Grade Papers Specification as per IEC 554-3-5 1984 39

Table 3.2: Characteristics Requirement of IS, IEC, and BS Specification for uninhibited

transformer oil 44

Table 3.3: Standards and Testing 47

Table 3.4: Relationship between IFT vs NN 51

Table 3.5: Permissible limits as per IS 1866-2000 52

Table 3.6: Initial samples parameters 52

Table 3.7: Thermal degradation test on pure transformer oil 53

Table 3.8: Thermal degradation test on oil along with Press board and Kraft paper 54

Table 3.9: Influence of water content 55

Table 3.10: Significance of test 56

Table 4.1: Recommended safe value of various gases in [µl/L(ppm)] 62

Table 4.2: Permissible concentration of dissolved gases on ageing 62

Table 4.3: Key Gases generated by particular fault 63

Table 4.4: Dissolved gases in transformer oil 75

Table 4.5: California State University Sacremento Guidelines for Combustible Gas 78

Table 4.6: California State University Sacremento Guidelines for Combustible Gas 79

Table 4.7: CEGB/ANSI/IEEE Guide for gas concentration limits in ppm V/V 79

Table 4.8: Other International Gas Concentration Limits in ppm V/V 80

Table 4.9: SERC – Japan limiting values in ppm V/V 80

Table 4.10: EDF – France Generator/Transmission transformers without OLTCs 81

Table 4.11: Concentration limits of dissolved gas 84

Table 4.12: Dorenburg‘s ratios for key gases 84

Table 4.13: Roger‘s ratios for key gases 86

XIX

Table 4.14: IEC 60599 Ratio-Fault diagnosis 89

Table 4.15: IEC 60599 Typical gas levels 89

Table 4.16: Relative Performance of Different Methods 92

Table 5.1: Uncertainty on laboratory results 97

Table 5.2: Ranges of 90% typical values for power transformer in ppm 98

Table 5.3: Ranges of 90% typical rate of gas increase for power transformer

in ppm/year 99

Table 5.4: Pre-failure gas concentration values at CIGRE for core- type transformers 100

Table 5.5; Numerical zone boundaries 100

Table 5.6: Triangular coordinates for DTM 1 zones 101

Table 5.7: Identification of Fault Zones in above OLTC figure 105

Table 5.8: Numerical zone boundaries of Duval triangle 2 106

Table 5.9: Numerical zones of DTM 2 106

Table 5.10: Triangular coordinates for Duval triangle 2 zones 107

Table 5.11: Zone Boundaries (% C2H4) for Duval Triangle 3 (non-mineral oils) 110

Table 5.12: Numerical zone boundaries for DTM 3 (Silicone oil) 110

Table 5.13: Triangular coordinates for DTM 3 zones (Silicone Oil) 110

Table 5.14: Numerical zone boundaries for DTM 3 (Midel oil) 112

Table 5.15: Triangular coordinates for DTM 3 zones (Midel Oil) 112

Table 5.16: Numerical zone boundaries for DTM 3 (FR3 oil) 114

Table 5.17: Triangular coordinates for DTM3 zones (FR3 Oil) 114

Table 5.18: Numerical zone boundaries for DTM 3 (BioTemp oil) 115

Table 5.19: Triangular coordinates for DTM3 zones (BioTemp oil) 116

Table 5.20: Duval triangle method 4 fault zones identifications 118

Table 5.21: Duval triangle method 4 numerical zone boundary 118

Table 5.22: Numerical zone boundaries for DTM 4 (low intensity faults) 119

Table 5.23: Triangular coordinates for DTM 4 zones 119

Table 5.24: Duval Triangle method 5 fault zones identifications 121

Table 5.25: Duval Triangle method 5 numerical zone boundaries 121

Table 5.26: Numerical zone boundaries for DTM 5 122

Table 5.27: Triangular coordinates for DTM 5 Zones 122

Table 5.28: Comparative analysis between simulated and graphical fault interpretations in

Duval triangle 1 126

XX

Table 5.29: DTM1 simulated fault diagnosis compared with Roger‘s and Dornenburg‘s

ratio fault interpretations 130

Table 6.1: Membership function ranges for three gas inputs of DTM1 139

Table 6.2: Transformer fault outputs DTM 1 140

Table 6.3: Fuzzy Rules for DTM 1 142

Table 6.4: DTM2 ranges of input gases 144

Table 6.5: Types of faults in OLTCs 145

Table 6.6: Fuzzy rules for OLTC faults 146

Table 6.7: Input Ranges for DTM3, Silicone oil 147

Table 6.8: Fault output membership functions of DTM3, Silicone oil 149

Table 6.9: Fuzzy Rules for DTM3, Silicone oil 150

Table 6.10: Ranges of input for DTM3, Midel oil 152

Table 6.11: Output ranges for DTM3, Midel oil 154

Table 6.12: Fuzzy rules for DTM3, Midel oil 155

Table 6.13: Input ranges for DTM3, FR3 oil 157

Table 6.14: Fault output for DTM3, FR3 oil 158

Table 6.15: Fuzzy rules for DTM3, FR3 oil 160

Table 6.16: Input ranges for DTM3, BioTemp oil 162

Table 6.17: Fault output for DTM3 BioTemp 163

Table 6.18: Fuzzy rules for DTM3, BioTemp oil 165

Table 6.19: Input ranges for DTM4 167

Table 6.20: DTM4 output ranges 168

Table 6.21: Fuzzy rules for DTM4 169

Table 6.22: Input ranges of DTM5 170

Table 6.23: Fault outputs in DTM5 172

Table 6.24: Fuzzy rules for DTM5 173

Table 6.25: Parameters for ANN Duval triangle method 1 179


Table 6.27: Parameters for ANN Duval triangle method 3, silicone oil 185

Table 6.28: Parameters for ANN DTM 3, Midel oil 187

Table 6.29: Parameters for ANN Duval triangle method 3, FR3 oil 191

Table 6.30: Parameters for ANN Duval triangle method 3, BioTemp oil 194



XXI

Table 6.33: Comparative results of DTM1 simulation and Fuzzy logic DTM1 201

Table 6.34: Comparison of results simulation and ANN 202

Table 6.35: Comparison of FL and ANN results 203

Table A-1: Gas concentrations 214

Table A-2: Gas generating rate 214


Table A-4: H2 generating rate 215

Table A-5: Roger‘s Ratio fault diagnosis 215

Table A-6: Dornenburg‘s ratio fault diagnosis 216

Table A-7: Gas concentration 217


XXII

ABRIBEATIONS AND NOTATIONS

AEG- Allgemeine Elektrizitäts-Gesellschaft (AEG) (literally General Electricity

Company)

AT&C- Aggregate Technical & Commercial

ASTM- American Society for Testing and Materials

ANSI- American National Standards Institute

AI- Artificial Intelligence

ANN- Artificial Neural Network

AWNN- Fuzzy Wavelet Neural Network

BBC- Brown, Boveri & Cie (BBC), a Swiss group of electrical engineering

BP- Back Propagation

BP-ALM- BP-Adaptive Learning and Momentum

BS- Bureau of Standards

CT- Current Transformer

CEA- Central Electricity Authority of India

CO- Carbon Monoxide

CO2- Carbon Dioxide

CH4- Methane

C2H4- Ethylene

C2H6- Ethane

C2H2- Acetylene

CEBG- Central Electricity Generating Board in UK

CMAC- Cerebellar Model Articulation Controller

CIGRE- International Council on Large Electric Systems in France

CRGO- Cold Rolled Grain Oriented

CCR- Continuous Casting Rolling

DT- Distribution Transformer

DT- Thermal Electrical Fault

DTM- Duval Triangle Method

DGA- Dissolved Gas Analysis

DP- Degree of Polymerization

DF- Dissipation Factor

D1- Discharge of Low Energy

D2- Discharge of High Energy

ES- Expert System

EDF- Energias de Portugal, S.A

ECRA- Electrical Cooperative Research Association

EHV- Extra High Voltage

ET- Evidence Theory

eV- Electron Volt

FCM- Fuzzy C Mean

FIT- Flame Ionization Detector

GCA- Grey Clustering Analysis

GRNN- Generalized Regression Neural Network

GC- Gas Chromatography

http://en.wikipedia.org/wiki/Swiss

http://en.wikipedia.org/wiki/Electrical_engineering

XXIII

GM- Grey Model

GA- Genetic Algorithm

GDR- Generalized Delta Rule

HV- High Voltage

H2O- Hydrogen Oxide

H2- Hydrogen

H- Hydrogen Molecule

HED- High Energy Discharge

IEC- International Electrotechniical Commission

IEEE- Institute of Electrical & Electronics Engineering

IS- Indian Standard

ISO- Organization of International Standard

IFT- Interfacial Tension

KV- Kilo Volt KOH- Potassium Hydroxide

LV- Low Voltage LT- Low Tension

LTC - Load Tap Changers

LS- Least Square

LED- Low Energy Discharge

MVA- Mega Volt Ampere

MU- Mega Unit

µL/L- Micro Litre / Litre

M*- Molecule

NN- Acid Number

NBR- Brazil Standards: Associação Brasileira de Normas Técnicas

(ABNT/NBR)

NEMA- National Electrical Manufacturers Association, U.S.

OH- Hydroxyl

O2- Oxygen

OLTC- On-line Tap Changer

PT- Potential Transformer

PF- Power Factor

PD- Partial Discharge

ppm- Parts Per Million

PSO- Particle Swarm Optimization

PFGC- Pre- Failure Gases Theory

PFS- Pre- Failure values

RF- Radio Frequency

FIS- Fuzzy Inference Systems

RS- Rough Set

RBFNN- Radial Basis Fuzzy Neural Network

RTR- Refined Three Ratio

SECR- Japan

SSC- Sum Square Error

SVM- Support Vector Machine

SVMG- Support Vector Machine with Genetic Algorithm

T&C- Technical & Commercial

UHV- Ultra High Voltage

TPMO-

XXIV

T1- Thermal Fault <300o

T2- Thermal Fault 300-7000c

T3- Thermal Fault >7000c

TCG- Total Combustible Gases

TDCG- Total Dissolved Combustible Gases

UAT- Unit Auxiliary Transformers TCD- Thermal Conductivity Detector

WNN- Wavelet Neural Network

WNNGA- WNN Genetic Algorithm

Special Abbreviations Used for fault comparative

tables

Cir. C Circulating Current

NF No Fault

Ind. Individual

PD Partial Discharge

N Normal

SG Gases

IG Individual Gases

S OK Sample OK

NA No Abnormality

DZD Denominator Zero Diagnosis

DZD B DZD Bypassed

MF Mixed Fault

DUVAL Duval Triangle

SRQ Sample req. quarterly

T Testing Required

Reco. Recommendation

AD Arc Discharge

Comments Comments

NI Not Identified

NG Not Given

TF Thermal Fault

DA Discharge Arcing

NF No Faults

1

CHAPTER-1

INTRODUCTION

1.1 INTRODUCTION

Electrical energy is the backbone of the economy of a country. The trend towards a

deregulated global electrical energy market has put the electric utilities under severe

stress to reduce operating cost, increase in the availability of the generation, transmission

and distribution sub-assemblies and to improve the supply from power system. Hence the

function of power system is to supply electric energy to its user with an acceptable degree

of reliability, quality and economy. Power transformer plays a critical role in efficient and

effective transmission, distribution and utilization of electrical power. It makes possible

electric generation at the most economical generator voltage, power transfer at the

economical transmission voltage and power utilization at the most suitable voltage for the

particular utilization device. Power transformer is the heart of the power system network.

Power system reliability can improve when transformer incipient faults are detected and

eliminated before it leads to transformer failures.

1.4 TRANSFORMER

Transformer is an electrical device used to transfer an alternating current or voltage from

one electric circuit to another by means of electromagnetic induction. The simplest type

of transformer consists of two coils of wire, electrically insulated from each other and

arranged in much a way that a change in the current in one coil (the primary) will produce

a change in voltage in the other (the secondary). In many transformers, the coils are

wound on a core made of a material with high magnetic permeability. This intensifies the

magnetic field induced by the current in the primary, increasing the transformer's

efficiency. Neglecting power losses, which are made small by careful design, the ratio of

primary voltage to secondary voltage is the same as the ratio of the number of turns in the

primary coil to the number of turns in the secondary coil. The primary and secondary

currents are in inverse proportion to the number of turns in the coils. The primary and

secondary impedances are in the same ratio as the squares of the numbers of turns in the

primary and secondary coils. Transformers are frequently classified according to their

uses; the details of construction depend on the intended application. Power transformers

are generally used to transmit power at a constant frequency.

2

1.5 TYPES OF TRANSFORMERS

Power Transformers are the main types of transformers, and among these the most

representative group is the double-wound power transformers, which are used on power

lines. Power transformers raise the voltage of the current generated at electric power

plants from 10–15 kilovolts (kV) up to 220–750 kV, thus making possible the

transmission of electric power over aerial transmission lines for several thousand

kilometers. At locations where electric power is consumed, the high voltage is stepped

down to low voltages (220 V, 380 V, and so on) by other power transformers. The

repeated transformation of the electric power requires a large number of power

transformers, and therefore the total power of the transformers in the system is several

times that of the sources and power users. High-capacity power transformers have an

efficiency of 98–99 percent. Their windings are generally made of copper, and their

magnetic circuits of cold-rolled sheets of electrical steel 0.5–0.35 mm thick that have high

magnetic permeability and low hysteresis and eddy-current losses. The magnetic circuit

and windings of power transformers are usually mounted in a tank filled with mineral oil,

which is used to insulate and cool the windings. Such transformers—called oil-filled

transformers—are generally located in the open, which require improved insulation of the

leads and hermetic sealing of the tank. Transformers without oil cooling are called dry

transformers. For better heat dissipation, power transformers are equipped with a tubular

radiator around which the air or, in some cases, water circulates. In lightning-proof

transformers the windings are designed so as to avoid the appearance of dangerous

voltages across the insulation. Sometimes two or three transformers are connected in

series. In many cases transformers with load tap changing are used. Among the dry

transformers, low-power transformers with a large number of secondary windings (multi-

winding transformers) make up a broad class.

In addition to power transformers, there are many types designed to measure high

voltages and currents (for example, instrument transformers, voltage transformers, and

current transformers), to reduce the level of interference on wire communication lines

(negative booster transformers), to convert a sine voltage to a pulse voltage (peaking

transformers), to convert current and voltage pulses (peak transformers), to isolate an AC

current component, and to decouple and match parts of an electric circuit.

Power transformers are used on primary transmission lines for transmission and

distribution of relatively large amounts of power. The power transformer should

withstand stresses occurring during system disturbances and should be designed to satisfy

3

the requirements of the power system. They should show good correlation between actual

and optimum values of cost ratio. Power transformers are designed for maximum

efficiency at near or full load. Thus power transformers are simplest equipments in power

systems, which required great reliability for its life tenure of 30-40 years. Power

transformers are designed to be operated in very convenient manner and easy to maintain

or repair without removing from the service. Therefore, it is required to design

transformers with rare troubles.

Various types of power transformers are as follows:

i. Generator transformer

Transformer connected to generators usually supply power to a transmission line which

runs from generating plant to a bulk power load centre located at considerable distance

away, some of the general requirements of a transformers are as follows:

a) No voltage- regulating winding, because the voltage is regulated by the field of the

generator. Generally, it is provided with off-circuit tap changer with a small variation in

voltages. However in certain cases OLTCs are required for reactive power control of the

system.

b) Fairly, uniform load, the units of high efficiency in particular are kept loaded to the

maximum capacity.

c) Generally provided with unit cooler/ water heat exchanger cooling arrangement.

d) It must take over excitation during operation.

e) Special care to avoid any hot spot in the conducting metallic parts in its vicinity; the

epoxy bonded completely transposed conductor (CTC) is preferred for LV winding to

minimize eddy current losses and provide greater short-circuit strength.

ii. Unit auxiliary transformers (UAT)

These are step-down transformer with primary is connected directly to generator output.

The secondary voltage is of the order of 6.6 KV for supplying to various auxiliary

equipments in the generating station. The voltage rating should be specified in such a way

that the secondary voltages are about 5% more than the nominal voltages to compensate

the transformer regulation. The capacity of these transformers is based on the auxiliary

load of the particular unit with the 10% margin.

iii. Station transformers

These transformers are required to supply auxiliary equipments during setting-up a

generating station and subsequently during starting-up operation. The rating of these

transformers is small, and their winding is connected to a high voltage transmission line.

4

The split secondary winding is often employed to have economical circuit breaker ratings.

These transformers are extensively used in thermal power plants to feed power to boiler

feeding pump motors, induced drought fans etc. other characteristics are similar to

network transformers. The capacity of station transformers should meet the following

requirements:

a) All running auxiliaries of station services at full station load,

b) The running auxiliaries of one of the unit bus at full load in the event of outages of

one UAT at time in the power station and

c) The auxiliaries starting of other unit which may be taken as 40% of the normal

auxiliary load.

iv. Inter-connected transformers

Normally, autotransformers are used to interconnect two-grids/systems operating at two

different voltages. They are located in the transmission system between generating

transformers and receiving transformers and they reduce transmission voltage to sub-

transmission voltage level. They are characterized by wide tapping range and additional

tertiary windings, which may be loaded or unloaded. Synchronous condenser or the shunt

reactors are connected to tertiary windings for reactive power compensation. In case of

loaded secondary conductor area and proper supporting arrangement are required for

withstanding S.C. forces under asymmetrical fault conditions

v. Receiving station transformer

These transformers are normally step-down transformer reducing transmission / sub-

transmission voltage to primary feeder level (33kV). Some of these may be directly

supplying power to industries. Loads on these transformers vary under wider limits and

the losses are expensive. If these transformer, when they close to residential area, it is

desirable to have lower noise level.

vi. Distribution transformer

Distribution transformers are used for distribution of power transmission lines and

network for local consumption. These are used for domestic / industrial uses. A lower

value of no-load loss is desirable to improve all-day efficiency. They are designed with

smaller values of iron losses relative to the full load losses. They show that the actual cost

ratio are high and mass ratio are lower than optimum. A great variety of transformers fall

into this category due to many arrangements and connections. Load on these transformers

varies widely and frequently overloaded.

5

vii. Earthing or grounding transformers

These are used to get a neutral point that facilitates the grounding and detection of earth

faults in an undergrounded part of a network (e.g. delta connected system). The sole duty

of a grounding transformer is to pass ground current during earth fault. The windings are

usually connected in inter-connected star (zigzag). These transformers (zigzag) have the

advantage that that are not affected by D.C magnetisation. The addition functions are:

a) Holding the neutral shift within limits,

b) Permitting the circulation of unbalanced load in the neutral,

c) To limit the current during line to earth fault,

d) To earth the system.

viii. Phase shifting transformers

These are used to control the power flow over the transmission line by varying the phase

angle between the input and output voltage transformer. Through a proper tap change, the

output voltage can be made to either lead or lag the input voltage. The amount of phase

shift required directly affects the rating and size of the transformer.

1.3 CHOICE OF VOLTAGES

Electric supply on its way from generating station to consumer end transformed several

(5-8) times. This energy demands not only needs best design and manufacturing

processes of the transformers to reduce the losses but also needs high voltage handling

capacities. Transformers having nearly constant ratio at all loads are generally used for

transmission and distribution system. These are said power transformers. The loads and

various components of total power are operated on their suitable voltage levels.

Therefore, high voltage transformer voltage choices for techno- economic considerations

are as follow:

i. Generation transformers

Generation transformer are connected to the generators to supply to transmission lines,

which is running between generating plant to the power load centre situated at a place

away. The generation voltages are (2.2, 3.3, 6.6, 11, 22, 33KV).

ii. Transmission transformers

Transmission needs step-up transformers with primary connected to directly to generator

output and secondary connected to transmission lines. These step-up transmission

voltages are (66, 132, 220, 440, 765KV).

6

iii. Distribution transformers

Distribution systems need step-down transformers to reduce transmission/sub-

transmission voltages to primary feeder levels. Further distribution transformers are used

for distribution of power from transmission lines network for local consumption. These

voltages are in the ranges of (3.3, 6.6, 11, 22, 33KV).

iv. Utilization transformers

Utilization generators are generally less than 1000 volts, 433 volts and 250 volts for

power and lighting. These voltages are used for domestic and industrial applications.

1.5 SPECIFICATIONS WITH DESIGN PRACTICES OF POWER

TRANSFORMER

Reliability of the power transformer is linked with failure rate and mode of failure.

Various parts of the transformers viz magnetic circuit, insulation system, termination,

cooling equipments etc, are designed as per specification details. Thus the magnetic

circuit is designed based on continuous and short time power frequency over voltages and

its suitability depends on the design, material selection, the quality of manufacture,

careful handling etc. For the average transformer used in a power station, the conductor

used for the windings consists of paper insulated copper bar or wire. In assembling the

transformer, great care is taken to ensure windings are well insulated both from the iron

core and from each other. The assembled transformer has its winding and iron core

assembly usually contained in a tank and immersed in transformer oil. The oil is used for

further insulating purposes plus the removal of heat from the windings. The assembly of

the windings on the core allows gaps to enhance the oil circulation around the windings.

The tank is constructed with fins or tubes to allow better circulation of the oil and to

provide a greater surface area for contact with the cooling air.

Active materials of the power transformers are conductors and magnetic materials serving

to create the most favorable condition for the electromagnetic process-taking place in the

equipment. In power transformers, transformer action takes place through core and coils

(active materials). Transformer components are to be compared to human body like core-

bone, winding-heart, insulation-flesh, oil-blood, and tank-skin. All the parts of the

transformer must function properly to its best performance. Thus major materials to

fabricate the parts of the transformer must be given proper attention and quality checks.

Some of the major materials in Table 1.1 are as follows;

7

Table 1.1: Material used for transformer manufacturing

Sl. No. Item Materials

1 Magnetic

core

CRGO of various types

Amorphous iron(Advantageous for distribution

transformers)

2 Conductor Copper and aluminum in the form of wire, strip or sheet

Better quality of conducting materials are produced by

continuous casting rolling (CCR process), which can

produce strip/sheet or wire in longer length, which avoids

welded or brazed joints. Completely transposed coils are

essential in certain cases

Replacing strip of LV winding

3 Coolant Transformer oil

4 Insulating

materials

Cellulose pres-board, raft paper, synthetic, self-bonding

paper

Classification of insulating materials: The recommended

thermal classes are 90, 105, 120, 130, 155, 180 and above

(in oC

) [Y, A, E, B, F, H & C]

The different properties of materials are divided into three groups:

i. Technology properties

Power transformer should possess the reasonably good mechanical properties which are

high castability, forgeability, weldability and machineability. All the materials used for

making the parts of the power transformers are required to have high wear resistance and

cold workability. Since transformer temperature always goes above the couples of

hundreds of degrees for the solid and liquid materials used in it. Thus, the material used

also posses the high degree of hot strength and hot rigidity.

ii. Physical properties

Physical properties of transformer includes mechanical properties, thermal properties,

electrical properties, magnetic properties and optical properties. The thermal properties a

power transformer should have high melting and boiling points, low thermal expansions,

high thermal capacity and good thermal conductivity. As a part of mechanical properties

the materials used will have good elasticity and plasticity, high tensile and compressive

8

strength, and very high hardness, low brittleness to withstand any kind of external

damages. Among the electrical properties conductors used must have high conductivity.

All the insulating materials used in transformer manufacturing are required to possess

high dielectric strength and good trekking resistance. Core in the transformers occupies

the maximum area and weight, thus core material would have high permeability, coercive

strength but it should have low resonance power. In optical properties materials should

hold the good colour, gloss and transparency wherever such materials are required and

applicable.

iii. Chemical properties

Since the temperature of the transformers rapidly changes and also exposed to

environmental changes thus it becomes desirable to have high degree of chemical

properties those are high corrosive resistance, high acid resistance, alkali resistance and

scaling resistance for the entire working life of the transformers.

Since core in the transformer is the biggest portion, therefore basic requirements of the

material used in transformer core which are to be taken care off includes:

i. Low hysteresis loss

ii. Low eddy-current loss

iii. High saturation flux density

iv. Long life

v. Easy to manufacture

vi. Low specific apparent –power input for no-load current

vii. Low magnetostriction for low noise level

viii. High grade insulation

ix. Good mechanical processing properties

x. Suitability for high flux densities

1.5.1 Basic Objectives of Specifications

Technical specification is a document which lays down characteristic of a power

transformer or a service as a level of quality, performance, safety or dimensions. It may

include or deal exclusively with the terminology, symbols, testing or test methods,

packaging, marking or labeling requirements. An important benefit of specification is

improvement and suitability of equipment and services for their intended purposes.

Transformers specifications shall necessary include design review / procedure and

process for manufacture and testing requirements. National and international standards

are performance required and prescribe the testing needed to confirm that standards. In

9

power transformers mainly two specifications ie standard specifications and consumer‘s

specifications decide the specifications of power transformer as follows:

i. To achieve optimum overall economy and efficiency.

ii. To establish proper co-ordination with other related equipments.

iii. To reduce cost, human efforts and conserve essential materials.

iv. To increase productivity, eliminate waste and reduce inventories.

v. To achieve countywide interchangeability of parts.

vi. To define quality and to recommend the mode of its operation.

Thus adhering to specifications at all levels becomes important. Some of the importance

considerations are as follows:

a) Increase efficiency, productivity and quality approved by National and International

standards.

b) Promote better applications and understandings of principles and techniques.

1.5.1.1 Specification for power transformers

The specification forms the basis on which the manufacture designs, manufactures and

test a power transformer. With the growth in size of power system and complexities of

modern system, the adequacy of transformer specifications plays an important role on its

reliability. A good specification must properly consider the effect of application, system

operating condition, environmental conditions site details, service conditions, termination

details etc so that the power transformer is designed for the conditions under which it has

to operate. Thus the adequacy of specification of power transformers particularly that of

high voltage and larger rating assumes tremendous importance and an inadequate

specification can lead to a deficient design and poor reliability. Mostly the following

standards are accepted in India:

i. IEC: 60404 – 8 – 7:1998

ii. IEC: 60076 part 1-5

iii. Indian standard IS 3024:2002 and

iv. IS: 2026 part 1-5

v. IEEE C57-12.00

1.5.2 Design Practices of Power Transformers

The basic materials of construction of oil immersed transformers have not changed

dramatically in the last century, but there are large improvements in quality and their

performance qualities. Designers are taking full advantage of these developments in

power transformers with the state of the art of design methodologies. However, there

10

shall not be any relaxation in quality margins. On the operational side also, rapidly

development power system have identified more areas for concerns such as:

i. Very fast fronted transients specially for GIS application

ii. Part winding resonance

iii. Static electrification

iv. Geomagnetic disturbances

v. Quality and purity of supply

Despite the presence of international and national standards defining the performance and

the testing requirement, the actual service requirement are not reflected in these standards.

A power transformer designed to meet the international and national specifications may

not be completely appropriate in the actual operating conditions. The power transformers

specifications should include

i. Environmental condition

ii. Anticipated system condition, voltage and frequency profile

iii. Loadability limits under varying frequency trend

iv. External short-circuit scenario and

v. Asthetics.

Power transformer design involves many interdependent parameters. It becomes very

difficult to optimize a power transformer design. Most power transformer designers use

an electrical model that allows them to approximate a transformer design. The

preliminary approximate design will be evaluated, and then adjusted as needed to

achieve desired objectives. Table 1.2 shows the technical specification of a power

transformer. Here core size is optimized for minimum material price as prime objective in

design.

Table 1.2: General Information & Technical Specification

Input voltage 3 x 35000/20230V, star sine wave

Transformer output voltage 3 x 690/400V, star

Line output current 3 x 1340A, continuous operating mode

Frequency 50Hz

Average oil temperature 55°C

Max. temperature rise and/or

max. Cu-winding losses at

75°C

25°K

18000W => 1.125%

11

Short-circuit voltage 6.5%

°Short-circuit voltage 6.5%

Max. core losses 3200W => 0.2%

Max. no-load current 1.3%

Test Voltage at 50Hz, 1

minute

Primary 85kV, outside Secondary 4kV,

inside Steel & Core Assembly M5, annealed, strips for alternated stacking

(4x45°+3x90° per shape), "round" cross

section with 8 steps

Core Size Optimized for minimal material price for:

Cu_Price/Fe_Price = 2 with Cu-winding

1.5.2.1 Basic physical technologies

To make reliable designs for large power and high voltage/extra high voltage, there must

be good theoretical understanding of physics. The basic physical areas like to:

i. Dielectrics control the complete insulation structures (for EHV / UHV );

ii. Acoustics achieve lower sound level;

iii. Load losses, thermal performances control of losses at load and the temperatures in

the transformer created by those losses;

iv. Short circuit strength make mechanical stable design due to all kinds of external

short circuits; and

v. Transients and fields ( using sophisticated 2D and 3D tools) to simulate lighting

impulse phenomena and electromagnetic fields from high currents.

1.5.2.2 Requirements

The main design requirements for a good transformer are:

i. Primary inductance should be as high as possible to reduce no-load current to a small

value;

ii. A high coefficient of coupling between primary and secondary winding to ensure

small voltage regulation under load;

iii. Core losses as low as possible by;

a) Choice of suitable core materials.

b) Lamination of core.

iv. Winding losses to be as low as possible by;

a) Use of high conductivity winding copper wire of adequate cross sectional area.

b) Keeping the number of turns in the primary winding as small.

12

v. Size of transformer to be as small as possible by;

a) Running it at as high a temperature as allowable.

b) Use of conductor and core materials of good space factor.

vi. Efficiency to be high by keeping losses low (No.3 and 4);

vii. Weight to be low;

viii. Reliability to be high by;

a) Choice of materials and types of construction.

b) Use of adequate space factors.

c) Adequate sealing against moisture.

d) Robust mechanical construction.

1.6 POWER LOSSES

Whenever the voltage applied to induction motor varied from rated voltage, its

performance is affected. Within permissible voltage of +/- 6% of the affect practice, the

supply voltage varies by more than 10% in many distribution systems. A reduced voltage

in case of induction motor results in higher currents drawn for the same output. For a

voltage drop of 10%, the full load current drawn by the induction motors increase by

about 10% to 15%. The starting torque decreases by nearly 19% and the line losses in the

distributor increases by about 20%. As the bulk load of rural areas and small scale

industrial areas consist of induction motors, the line losses in the concerned distribution

systems may even touch 20%. Power losses in transmission and distribution systems are

as follows:

1.6.1 Technical Loss

Every element in a power System (a line or a Power transformer etc) offers resistance to

power flow and thus consumes some energy while performing the duty expected of it.

The cumulative energy consumed by all these elements is classified as Technical Loss.

1.6.2 Commercial Loss

Losses occur on account of non-performing and underperforming meters, wrong

applications of multiplying factors, defects in CT & PT circuitry, meters not read,

pilferage by manipulating or by passing of meters, theft by direct tapping etc. These are

all due to non-metering of actual consumption and are called commercial losses. The

total of Technical and Commercial losses are termed as T&D loss. It is unfortunate that

in addition to the above, there is also a loss in revenue due to non-realisation of billed

demand. This is in addition to commercial losses and the aggregate of T&D loss and

revenue loss due to non-realisation is termed as AT&C loss (Aggregate Technical and

13

Commercial loss). Therefore, AT&C loss to the utility is the sum total of technical loss,

commercial losses and shortage due to non-realisation of total billed demand. Table 1.3

shows an example of AT&C losses.

Table 1.3: Calculation of AT&C Losses

1. Units Input: 100MU: Revenue collection with reference to billed demand 90%

2. Unit Billed: 70MU: This means out of 70 MU sales realisation of 90% 63%

3. T & D Losses: 39MU: ATC losses 37%

Power distribution systems in developing countries had to face phenomenal and rapid

growth of load in the last two decades. Distribution systems were expanded on adhoc and

haphazard basis keeping minimum investments in view.

Power systems are highly cost intensive and the investments needed to reduce technical

losses by every 1% are too high. Computer aided load flow studies are to be made to

arrive at peak power loss and also to arrive at technical losses based on load-duration

curves. The Transmission system planning is done on a separate footing that is keeping

in view system adequacy, system security, reliability etc for n-1 and n-2. Conditions as

per guidelines are given by CEA. Losses in transmission systems are too small and loss

reduction is not an issue of focus in Transmission system planning.

1.7 POWER SYSTEM RELATED LOSSES IN POWER TRANSFORMERS

Power transformer losses are reduced to minimum by observing proper design practices,

manufacturing processes, observing proper standards at all the stages. Still there are many

losses associated with power transformers during distribution process. Some of the losses

are as follows:

1.7.1 Distribution Transformers (DTs) not Located at Load Center on the Secondary

Distribution System

Often DTs are not located centrally with respect to consumers. Consequently, the farthest

consumers obtain an extremity low voltage even though a reasonably good voltage levels

maintained at the transformers secondaries. This again leads to higher line losses.

Therefore in order to reduce the voltage drop in the line to the farthest consumers, the

distribution transformer should be located at the load center to keep voltage drop within

permissible limits.

14

1.7.2 Over-Rated Distribution Transformers and hence their Under-Utilization

Studies on 11 KV feeder has revealed that often the rating of distribution transformers is

much higher than the maximum KVA demand on the feeder. Over rated transformers

draw an unnecessary high iron loss. In addition to these iron losses in over rated

transformers the capital costs locked up is high.

From the above it is clear that the rating of distribution transformers should be judiciously

selected to keep the losses with the permissible limits.

For an existing distribution system the appropriate capacity of distribution transformer

may be taken as very nearly equal to the maximum KVA demand at good PF (say 0.85)

Such an exercise has been carried out for a number of distribution systems and

transformers with capacity of 25, 63, 100, 160, 315 KVA and standardized for different

with power factors and diversity factors.

1.8 SELECTION OF POWER TRANSFORMERS

For selection of power transformers, Indian standards code of practice for selection,

installation and maintenance of transformers IS:10028 (part 1) selection should be

adhered to. There are several basic considerations which must be included by the system

engineer to select and design the best voltage which should supply power to both present

and future loads economically. Some of these are:

i. Safety It is desirable to achieve high degree of safety of equipment and the personnel

working on power transformers and the adjoining area.

ii. Reliability Power transformers are required to give high degree of reliability even in

stipulated operating conditions during its stipulated service span of at least 40 years

within working specifications. Power transformers are designed and manufactured to

meet the reliability and offer trouble free service.

iii. Maintenance Power transformers regular maintenances are required to ensure

reliable service throughout its life, put back a transformer which has broken-down into

satisfactory working condition in a minimum possible life and also to keep the

maintenance cost minimum. Thus transformer needs routine, corrective, preventive,

proactive and emergency maintenances during its service life.

iv. Voltage regulation There are possibilities of recurring overloads and exceptional

overloads in the working of power transformers. Thus under any overload conditions, it is

desirable from the power transformers to maintain high voltage regulation to keep the

output voltage constant.

15

v. Initial investment Every utility choose to keep its economy at the priority. Thus it

becomes necessary for every manufacturer to keep the cost of a power transformer in

competitive limits and to keep other required specifications within the ranges.

vi. Simplicity in operation Each user develops his own operation needs appropriate to

his requirements. Utilities also would want to operate power transformers with ease for

the entire life of it. Thus simplicity in operation of power transformer becomes prime

important.

vii. The overall system economics Starting from design, manufacturing, installation,

maintenance, repair and overhauls (if needed) involve economy. Though continuity of

power supply is needed from any transformer. The total cost of a transformer can be

considered as the assured annuity for the estimated life of the transformer obtained from a

capital sum invested. Therefore every efforts are made to compromise on the issues and

overall cost of the system tried to keep low.

1.9 POWER TRANSFORMER FAILURES AND PROBLEMS

Transformer failure can occur as a result of different causes and conditions. Generally,

transformer failures can be defined as follows:

i. Forced outage due to damage in service transformer (e.g. winding damage, tap

changer failure).

ii. Trouble that requires removal of the transformer for return to a repair facility, or

which requires extensive field repair (e.g., excessive gas production, high moisture

levels).

Transformer failures can broadly be categorized as electrical, mechanical, or thermal. The

cause of a failure can be internal or external. Table 1.4 lists typical causes of failures.

Table 1.4: Main causes of failure of transformer in service

No. Part Failures

1 36% Design defects

2 28% Manufacturing problems

3 13% Material defects

4 5% Poor Maintenance

5 4% Lightening surges

6 2% Short circuit

In addition to failures in the main tank, failures can also occur in the bushings, in the tap

changers, or in the transformer accessories. Published data based on failure statics of

16

transformers on Indian network is not readily available. A wide range survey by working

group of CIGRE study committee 12, led by Bossi, published [1, 2] the results of survey.

Failure surveys have revealed that failure in the transformers are because their major

component shown in Table 1.5.

Table 1.5: Components causing failure in service

No. Part Failure

1 29% Winding

2 29% Terminals

3 13% Tank & Dielectric fluid

4 13% OLTCs

5 11% Magnetic circuit

6 5% Other accessories

Causes of transformer failures further categories between two major sections as internal

causes and external causes. These are listed in Table 1.6 below.

Table 1.6: Typical causes of transformer failures

Internal Causes External Causes

Loss of Winding Clamping System Switching

Operations/Thermal

Insulation Deterioration Lighting Strikes/Temperature

Oxygen System faults (short circuit)

Overheating System Overload

Moisture

Solid contamination in the Insulating Oil

Partial Discharge

Design & Manufacturing Defects

Winding Resonance

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The failure pattern of transformers follows a ―bath-tub‖ curve. The first part of the curve

is failure due to infant mortality; the second part of the curve is the constant failure rate;

and the last part of the curve is failure due to old age. In addition to normal ageing, a

transformer may develop a fault that results in faster-than-normal ageing, resulting in a

higher probability of failure.

Power transformers have proven to be reliable in normal operation.. The end of life of a

transformer is typically defined as the loss of mechanical strength of the solid insulation

in the windings. These power transformers are at the last stage of the ―bath-tub‖ curve.

They are expected to have an increasing failure rate in the next few years.

A survey indicates that the main causes (51% of transformer failures in a five year period)

are due to the following problems:

i. Moisture, contamination and ageing which causes the transformers internal dielectric

strength to decrease,

ii. Damage to the winding or decompression of the winding under short Circuit forces, or

iii. Damage to the transformer bushings caused by loss of dielectric strength of the

internal insulation.

An American utility reported four single-phase EHV autotransformer failures due to

transformer winding resonance. All of the failures involved the breakdowns of the no load

tap changers immediately after the transmission system were energized.

1. 10 NECESSITY OF FAULT DIAGNOSIS

Generally speaking, the term ―monitoring‖ describes a basic parameter measurement with

threshold alarms. The term ―diagnostics‖ indicates the addition of sophisticated analysis,

such as an expert system capable of providing an assessment of equipment condition and

suggested actions.

There are a variety of tools available to evaluate the condition of transformers. They can

be separated into traditional diagnostic methods that have seen widespread use for many

years and nontraditional methods that range from methods that are starting to be used to

methods that are still in the research stage. Fault diagnosis of power transformers are

required for the reasons:

i. Early detection of incipient faults:

a) Avoid catastrophic outage

b) Provide basis for economic repair decision

ii. Efficiency and Management:

a) Maintenance management based on measurement and trend analysis

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b) Ageing process and residual life under control

The ultimate goal of on-time monitoring and diagnostic is continually to and accurately

diagnose the current condition and capability of the equipment, provide an alert ―just in

time‖ the maintenance action as and when required and remaining useful life of the

equipment.

Conditioning monitoring of power transformers by the combination of ―on and off‖ line

methods strongly reduces the risk of severe failures thereby provides a reliable electric

power supply. The most recent development of sensor technology such as electronic

Buchholz relay, OLTC monitoring, based on power consumption of the motor drive, on-

line PD measurement and anhydron with dissolved gas analysis are contributing very

much and gaining field application for early warning.

A comprehensive diagnostic however needs that in failure, results from on-line and off-

line measurement have to be put together for better proven evaluation methods /

procedures which can be achieved by proper coordination between utility and

manufactures.

sufficient money has been invested in the installed power equipment in the electrical

energy systems. Power transformers are an important part of it. They represent a valuable

asset for the utilities, and are expected to be functional over tens of years. In earlier days

it could happen that power transformers with long operational years and considerable

‗weak‘ or a potential source of trouble were ‗simply‘ replaced. Money was ‗no problem‘

and the final energy customer had to pay the bill in the end. Today the paradigm has

changed and considerable efforts are directed into finding new approaches using

transformer monitoring, diagnosis and life assessment.

The stronger constraints that have faced transformer users stem from two major factors:

The deregulation of the energy market and advancement in science and technology.

The deregulation pressures are reflected in efforts for:

i. Costs reduction, which is affecting maintenance, replacement, repair and upgrade

philosophies

ii. Increase power transformer availability, in order to assure power quality and customer

satisfaction

iii. Handling unprecedented power flow patterns in the system

Consequences of the changing situation on the market have been a trend to allow higher

transformer overloads, to work power transformers longer and to take all necessary

additional measures to achieve that. These two facts imply that the transformer user

19

accepts a higher risk in operation but at the same time a high availability is required.

However, the operation of power transformer units is desired to occur with as low as

possible costs and by many utilities there is a continuous reduction in staff and repair

facilities. These aspects bear an intrinsic contradiction.

This cannot be solved without improved means of transformer life assessment and

condition evaluation, monitoring and diagnosis. Another important aspect influencing the

need for condition evaluation is that there is an ageing population of transformers

installed at the industries and in the transmission and distribution networks. Several of

these were installed during a fairly narrow time span, say 25-30 years ago, when large

investments were made in the electric infrastructure. These transformers have been

exposed to various accumulative deterioration stresses and will eventually attain a higher

risk of failure and will approach their expected end of life. This fact may cause the owner

of the population to see an increased number of transformers being in the need of

refurbishment, repair or replacement if not jeopardizing the reliability or availability of

power. The life assessment and condition evaluation techniques thus take on the task of

assessment of the transformer on a broader basis in a holistic view.

1.11 BENEFITS OF FAULT DIAGNOSIS

Some of the benefits of the fault diagnosis of any equipment in general are as follows:

i. Timely filled measurements

ii. Confirmation, on the spot, of the presence of the faults gases in the event of alarms

iii. On time proactive decision making

iv. Reducing unplanned outages

v. More predictable and reliable maintenance schedules

vi. Prevention of catastrophic failure and destruction of peripheral equipment

vii. Reduced maintenance costs

viii. The results provide a quality control feature, limiting the probability of destructive

failures

This leads to improvements in operator safety and quality of supply, limiting the severity

of any damage incurred elimination of consequential repair activities and identifying the

root causes of failures. Information is provided on the plant operating life enabling

business decisions to be made either on plant refurbishment or replacement, safer work

environment. As transformers age, their internal condition degrades, which increases the

risk of failure.

20

Failures are usually triggered by severe conditions, such as lightning strikes, switching

transients, short-circuits, or other incidents. When the transformer is new, it has sufficient

electrical and mechanical strength to withstand unusual system conditions. As

transformers age, their insulation strength can degrade to the point that they cannot

withstand system events such as short-circuit faults or transient over voltages.

To prevent these failures and to maintain transformers in good operating condition is a

very important issue for utilities. Traditionally, routine preventative maintenance

programs combined with regular testing were used. With deregulation, it has become

increasingly necessary to reduce maintenance costs and equipment inventories. This has

led to reductions in routine maintenance. The need to reduce costs has also resulted in

reductions in spare transformer capacity and increases in average loading. There is also

trend in the industry to move from traditional time based maintenance programs to

condition-based maintenance. These changes occur at a time when the average age of the

transformers in service is increasing and approaching the end of nominal design life.

The change to condition-based maintenance has resulted in the reduction, or even

elimination, of routine time-based maintenance. Instead of doing maintenance at a regular

interval, maintenance is only carried out if the condition of the equipment requires it [1].

Hence, there is an increasing need for better no intrusive diagnostic and monitoring tools

to assess the internal condition of the transformers. If there is a problem, the transformer

can then be repaired or replaced before it fails.

Many testing and monitoring techniques have been used by utilities.

1.12 ORGANISATION OF THESIS

This thesis work has been organized as follows:

Chapter 1 briefly describes different transformers with their types, uses, choice of

voltages, general specifications and design practices in manufacturing, power losses in

distribution system and transformer itself for proper material, design and manufacturing

standards. Choice of transformer selection is also specified. Necessity of fault diagnosis

of power transformers is discussed.

Chapter 2 provides the detailed information regarding research and developments

relevant to research area of Ph. D. work. It involves the progress made for conventional

testing of transformer (liquid and solid) insulations to detect faults at early stage or pre

warnings. It also shows the advancements in fault diagnostics methods. Further it presents

the application of artificial intelligence (AI) in dissolved gas analysis for fault diagnosis

of power transformers.

21

Chapter 3 emphasizes the importance, quality, specifications and periodic monitoring of

transformer insulating oil. The following tests have been performed for thermal

degradation of oil in power transformer:

i. Acidity

ii. Water contents

iii. Breakdown Voltage (BDV)

iv. Volume resistivity

v. Tan-Delta

vi. Dielectric constant and

vii. Flash point

After analysis, following conclusions are drawn;

i. All tests can be performed for fault diagnosis (periodic test) as pre-warnings of the

faults.

ii. Type of faults and reasons for the faults are correlated to confirm the faults.

iii. Most of the solutions are regeneration of oil such as filtering, removal of water

contents and sludge removal.

Chapter 4 classifies various faults that can be detected with the help of DGA. Guidelines

for interpretation of faults adopted in various countries have been presented. The different

diagnosis standards and methods for interpretation of dissolved gases have been

explained. Some methods used by countries other than INDIA for fault diagnosis are also

listed with brief descriptions. Comparison of different DGA methods for fault diagnosis

of power transformers (key gas ratios, Doernenburg‘ ratio, Roger‘s ratios and Duval

triangle) have been discussed. Case studies have been carried out to show the advantages

of Duval triangle method (DTM) over other methods.

Chapter 5 describes the interpretation of DGA in power transformers using the DTMs. It

is also shown that the accuracy of DGA laboratory results can affect the reliability of

DGA diagnosis. In addition to the six zones of individual faults an intermediate zone of

fault DTM has been developed for varieties of electrical and thermal faults in the power

transformer. The following conclusions are drawn from the DTMs:

i. Duval triangle fault interpretation methods can be used graphically in a very easy

way and the evaluation of faults is also easy.

ii. Available eight Duval triangles are capable of fault diagnosis separately; such as

main transformer, online load tap-changers (OLTCs), faults with low energy, faults with

low temperatures and synthetic oils.

22

Chapter 6 deals with the application of artificial intelligence (AI) in fault diagnosis of

power transformers. Fuzzy logic (FL) and artificial neural network (ANN) have been

used in DTM for better interpretation of results. Comparison of simulated results using

FL, ANN and Fault diagnosis reports available from various organizations gives the

following conclusions:

i. Percentage of gases based rules in FL gives flexibility in Duval triangle methods to get

accurate analysis.

ii. Superior learning capabilities of ANN gives better and quicker analysis as compared to

other fault diagnosis methods.

Chapter 7 includes the conclusions and the suggestions for future work. Major

conclusions are as follows;

i. Critical specifications of transformer oil can be performed for fault

diagnosis/condition monitoring (periodic test) as pre-warnings of the faults (if any) after

thermal degradation of transformer oil test analysis.

ii.Type of fault and reasons for the faults are correlated to confirm the faults from the test

reports of critical specifications of oil.

iii. Most of the solutions are regeneration of oil such as filtering, removal of water


iv. After comparison of different fault diagnosis methods of power transformers, it is

found that Duval Triangle Methods (DTMs) give better results.

v. DTMs have satisfied the fault diagnosis for more than 95% accuracy compared to the

other fault diagnosis methods.

vi. Traces of one of the three gases can be helpful for early fault diagnosis in the power

transformers.

vii. Application of FL and ANN in DTMs can provide higher accuracy, low uncertainty

and quicker analysis in fault diagnosis of power transformers.

viii. It has been proved that ANN based DTMs can provide full solutions in fault diagnosis

of power transformers.

Further chapter gives following suggestions for future work.

i. Optimization techniques may be attempted for improvement of accuracy in fault

diagnosis of power transformers.

ii. Intelligent techniques may be applied to reduce the uncertainties among faults.

1.13 AUTHOUR’S CONTRIBUTION

For the completion of this Ph. D. thesis, the following sequence of work was carried out:

23

i. Since transformer oil is easily available to ascertain faults without removing the

transformer from service, testing of oil parameters has been carried out to establish

correlation among them.

ii. Collection and analysis of dissolved gas analysis (DGA) reports have been done from

different utilities and authorities.

iii. Comparisons of fault diagnosis methods have been done.

iv. Investigations of Duval triangle methods have been explored for fault diagnosis of

power transformers.

v. Applications of the fuzzy logic and artificial neural networks in Dual triangle methods

have been done and their comparison with conventional methods to diagnose the

faults.

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CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

Literature survey is the sine aqu non of the research. Literature survey provides idea of

the recent developments in the area of research and also the future scope of the work on

the subject. This chapter includes the complete study of relevant literature pertaining to

the Ph. D. area of work. The systemic analysis of research papers results into specific

observations for the purpose of defining the objectives for the Ph. D.

2.2 FAILURE SURVEY ON TRANSFORMERS

There are a number of failure mechanisms which affect the life expectancy of

transformers, and transformer failure can occur as a result of different causes and

conditions. Zhang et al [1] developed a methodology to use data acquisition derived from

condition monitoring and standards diagnosis for rehabilitation purposes of transformers.

There are a number of failure mechanisms which affect the life expectancy of

transformers, and transformer failure can happen as a result of different causes and

conditions shown in Fig. 2.1. Failure factors in transformers include electrical breakdown,

lightning, dielectric fault, loose connection, incorrect maintenance, moisture, excessive

overloading, and other causes [1]. Contamination, thermal aging, repetitive excessive

voltage stress, and mechanical deformation lead to electrical breakdown. Dielectric

failure is a common failure occurrence and can have a profound effect on useful life.

Contamination and thermal aging can be monitored through testing. Voltage stress can be

controlled by design of the transformer protection and operating procedures.

Figure 2.1: Causes of Transformer failure [1]

Failure statistics of the defective components shown in Fig. 2.2 can identify the areas

where failure reducing efforts will produce the better outcome. When analyzing the

failure causes, information on faults is given in load tap changer (LTC), bushing,

winding, tank, core, and relay [1-2].

25

Figure 2.2: Percentage of defective components of transformers [1, 2]

Fig. 2.3 shows the most common detection methods of transformer failures and the

percentage of all the detection methods representing studies conducted for many years:

relay test, inspection, turns ratio test (TTR), dissolved gas analysis (DGA), resistance test,

total combustible gas test (TCG), current test, power factor test, temperature test,

capacitance test [1].

Figure 2.3: Percentage of failure detection methods [2]

2.3 FAULT DIAGNOSIS OF POWER TRANSFORMERS

Gibeault et al [3] suggested that fault diagnosis of transformers creates the opportunity to

statistically plan and schedule outages and to manage equipment utilization and

availability. The transformer can be kept in service, sometimes under load condition,

when the fault is evolving at a moderate and predictable rate. This condition will prevent

loss of revenue and provide time to plan sequential repair or replace the transformer.

Thus on site repairs can improve the availability. Monitoring a fast developing fault and

evaluating its progress provides the necessary information to marshal all the essential

resources to react on time and reduce the overall diagnostic. Managing and extending the

life of power transformer requires continuous and reliable monitoring as the validity of

any action taken to affect the life of transformer, needs to be tested periodically.

Successful life management and extension of power transformers produce the financial

benefits to the organization.

26

Detection and monitoring of incipient faults work with all maintenance concepts. There

are Reliability Centered Maintenance (RCM) and Condition Monitoring Maintenance.

These are two emerging maintenance concepts that require detection of incipient faults

and monitoring of a development of failure condition.

Thermal and electrical faults may arise following short circuits, local overheating at hot

spots or leakage of flux and eddy current in the core, and PD or arcing at areas of high

stress. Decomposition products from breakdown of the oil, kraft paper and press board,

glues, etc are transported through the transformer by the coolant/insulation oil [4]. The

end of life of a transformer is typically defined as the lost of mechanical strength of the

solid insulation in the windings and decaying of insulating oil.

J. Lapworth [5] suggested new method of interpretation DGA scoring algorithm, which

has proved to be robust and effective in screening out normal DGA results, and shows

promise in improving the detection of developing problems. Sparling et al [6-7] have

shown, the need of transformer monitoring and diagnostic online [6], multi-gas online

monitoring has been incorporated in transformer nursing unit (TNU) [7], which is a fast

fourier infrared spectrometer analyses gases extracted by permeable membrane. John

Sabau et al [8] has shown the progress in DGA testing and provided evidence that in

certain cases the reclamation of oil aged reduces the level of gases generated. Thus by

selectively removing decay products from transformer oil, the DGA interpretation can be

significantly improved and the cost effectiveness of reclamation technology. Shahsiah et

al [9] proposed a new diffusion dynamic model for propagation of characteristic gases in

transformer oil-cellulose structure due to temperature variation. Sabau et al [10] again

suggested the new laboratory technique developed for hidden side effort. It describes the

relationship between the parity of oil and its stability under electrical stresses, and the

necessity for maintaining the liquid insulation in pristine condition through the entire life

time of a power transformer.

2.4 TESTING OF SOLID INSULATIONS

Cellulose is a natural polymer of glucose and it degrades slowly as the polymer chains

breakdown during service, releasing degradation products into the oil. The paper

eventually degrades to such an extent that it loses all its mechanical strength and becomes

susceptible to mechanical damage, which puts the electrical integrity of the equipment at

risk. The transformer is subjected to mechanical forces due to transportation,

electromagnetic forces caused by system short circuits, and inrush current. Vibration and

27

thermal forces generated by different thermal expansion rates in different materials, cause

long-term degradation of the paper.

Faults in paper are generally considered as more serious than faults in oil, because paper

is often located in areas of high electric field (in the windings, or as voltage barriers), and

the destruction of paper insulation may lead to short circuits or severe arcing. The faults

in paper insulation are rare. Their presence can be seen by any DGA techniques for

dissolved gases in transformer oil. Therefore, for fault diagnosis during service or after

failures and/or life estimations of working transformers purpose the degree of

polymerization (DP) of solid paper insulation sample from the transformer and the furanic

analysis of the oil are performed. The solid insulation testings are as follows:

2.4.1 Degree of Polymerization (DP)

Sans et al [11] made a large scale survey of furanic compounds from the transformers

service and tried to correlate the remaining life of the transformer from content of furfural

hydrates dissolved in the oil. DP offline test is useful to confirm the deterioration of paper

insulation to determine the remaining life of power transformers. The DP tests the

cellulose molecules, which are made up of long chain of glucose rings and responsible

for mechanical strength of the paper insulation. To perform D P test remove a sample of

the paper insulation about one centimeter square. Carry out the DP test in laboratory and

analyses the results for remaining life with the standard table used by the utility.

2.4.2 Furan Test

Cellulose insulation decomposes due to overheating and chemical reactions get dissolved

in the oil. To estimate paper deterioration online and fault analysis of power transformers,

the furan analyses are conducted along with DGA test on oil insulation sample in the

laboratories. R. Blue et al [12] constructed a novel optoelectronic sensor for the

determination of furfuraldehyde (FFA) in the transformer oil. This sensor is compact and

portable that can be used by a non specialist operator. Again, R. Blue et al [13] reported

first time the used the of fluorescence based measurement of concentration in transformer

oil. In healthy transformer, the furanic compounds are less than 100 ppb and for

significant damage to paper insulation from heat up to 70,000 ppb show the end of life of

paper insulation or of the transformer. Testing is done for five different furans which are

caused by different problems.

28

2.5 TESTING OF TRANSFORMER INSULATION OIL FOR FAULT

DIAGNOSIS IN POWER TRANSFORMERS

The development of several new laboratory testing procedure for mineral insulating oils

over past years has been the rewarding result of a cooperative research project design to

extend the life expectancy of aging power transformers by eliminating the causes of

premature oil deterioration. Han et al [14], in this literature survey emphasized on the

importance of condition monitoring (CM) to reduce operating cost, enhancing the

reliability of operation and improve power supply. This paper also pointed out the

potential benefits through the utilization of advanced signal processing and artificial

intelligence technique to develop C.M. schemes for transformers through gas in oil

analysis. Kohtoh et al [15, 16] shown in their study that electrical, physical and chemical

characteristics of transformers oils are deteriorating by increasing age an usage. The

electrical characteristics such as volume resistivity, loss tan. Breakdown voltage

interfacial tension, and values gets more affected. Abu-Siada et al [17] shown that

ultraviolet to visible spectral response based equipments are cheap, and correlation

between oil spectral and its furan contents. Authors introduced fuzzy logic approach the

establish transformer ageing oil UV- Vis spectral response.

Some of the oil characteristics are tested as per the standards shown below [18-22]:

2.5.1 Break-Down Voltage (BDV)

Break down voltage is the ability of oil to withstand stresses without failure. Breakdown

strength analysis of transformer oil gives effective results through which suitable

dielectric material for the related high voltage applications can be explored. The

Breakdown Voltage (BDV) test kit consists of two electrodes mounted on horizontal axis

with 2.5mm gap and enclosed in a glass chamber. Electrodes used in test apparatus are of

sphere-sphere electrode configuration. The kit is provided with standard oil test set up is

powered through a step up transformer capable of offering up to 60 kV. The tests are

carried as per Indian standard IS-6792 [18] specifications.

2.5.2 Flash Point

Flash point of a volatile liquid is the lowest temperature at which it can vaporize to form

an ignitable mixture in air. The Fire point is defined as the temperature at which the

vapour continues to burn after being ignited. It is the lowest temperature at which, on

further heating beyond the flash point, the sample support and combustion for five

seconds. Flash and fire point test is carried out in Pensky-Martens open cup apparatus as

per ASTM D-3828 [19].

29

2.5.3 Acidity

Acidity is a measure of free organic and inorganic acid present in the oil and expressed in

terms of milligrams of Potassium Hydroxide (KOH) required to neutralize the total free

acids in one gram of oil. Presence of these acid contents in oil causes detrimental to

insulation system and leads to corrosion of tank. Acids usually originate from oil

decomposition, oxidation products and external atmospheric sources. Acidity level of

transformer oil is measured as per British Standard BS2000 Part1 [20].

2.5.4 Viscosity

Viscosity of oil is the measure of the transformer oil resistance to shear. Viscosity is more

commonly known as resistance to flow for fluids. If lubricating oil is considered as a

series of fluid layers superimposed on each other, the viscosity of the oil is a measure of

the resistance to flow between the individual layers. A high viscosity implies high

resistances to flow while a low viscosity indicates a low resistance to flow. Viscosity

varies inversely with temperature. Viscosity is measured using Red wood viscometer at

the temperature of 300 C. The test is carried out as per ASTM D 2162-06, IP 70 [21].

2.5.5 Tan-delta, Resistivity and Dielectric Constant

For measuring these characteristics, measurement test kit consists of three terminal oil

cell with heating chamber for oil cell. The tests are performed as per IEC 60247 Standard

[22].

2.5.6 Loss Factor

Loss factor is a measure of imperfection of dielectric nature of oil. For a perfect dielectric

when applied with a sinusoidal ac voltage, the current flowing through it should lead the

voltage by 900. But it is not in the case of reality, the angle by which it is short of 90

0 is

called the loss angle. The Loss tangent is also known as dielectric dissipation factor,

which measures the leakage current through oil. It is the measure of contamination

products like moisture and oxidation products. Dielectric dissipation value should be low

for good transformer Oil. Resistivity is the most sensitive property of oil requiring highest

care for its proper determination. Resistivity in ohm cm is numerically equivalent to the

resistance between opposite faces of a centimeter cube of the liquid. Insulation resistance

of windings of a transformer is also dependent upon the resistivity of oil. A low value

indicates the presence of moisture and conductive contaminants. Tested as per standards

[22].

30

2.6 DGA FAULT DIAGNOSIS OF POWER TRANSFORMERS

Based on the relative` proportions of the gases formed, several DGA interpretation

methods are available for identification of faults (low and high energy electrical discharge

faults, PD, arcing, corona or hot spots, etc). acceptable and dangerous gas levels in

transformers thus appears to be significantly influenced by the types of faults involved as

well as by the type and the age of the oil-filled transformers.

2.6.1 On-line/Off-site DGA

Continuous on-line monitoring of the acknowledged key gas indicator or parameter of the

operation of the transformers, the dissolved gases involved are generally CO, CO2,H2,

CH4, C2H6, C2H4, C2H2. The solubility of these gases is dependent on the gas, the gassing

tendency, temperatures. For any given sample the absolute and relative concentration of

fault gas can be indicate the type, intensity and location of the fault. The laboratory

DGA interpretations may rely upon defined critical levels of gases or/and rates increase in

gas level. The various conventional fault interpretation methodologies, i.e. key gas ratios,

Dornenburg‘s ratios, Roger‘s ratios, and some other diagnostic procedures are adopted

around the world. Ruijin et al [23] have shown in their research, the application of back

propagation ANN learning on the ratios of six combustible gases from DGA can diagnose

the transformer faults. McGrail et al [24] suggested various gases analyzing sensors, both

infrared and oxide utilized to develop portable system for continuous monitoring. Samat

et al [25] used gas chromatography, allow the determination of the principle gases, then

DGA interpretation with on revise publication 599 for nature of faults with different

methods (LCIE-LABORELEC – ROGER – DEVIS - DORNENBURG) for monitoring

oil filled equipment. SJ Farrito indicated that DGA can now be accomplished without

high vacuum extraction of the gases from the oil, the alternate method suggest that the

direct injection available alternate technique that offers many advantages and a valuable

tool of evaluating the condition of power transformer [26]. Inoue et al [27] noted the

hydrogen gas in oil is always one of the decomposed gases, a portable oil dissolved

hydrogen detector was developed for quick field screening for oil filled equipment for

detailed inspection.

2.6.2 DGA Duval Triangle Methods

The main gases formed as a result of electrical and thermal faults in transformers, and are

evaluated by DGA, whose concentrations depends upon the type of fault. Duval triangle

is triangular representations of three gases CH4, C2H4 and C2H2 only, which represents 7

faults zones within an equilateral triangle (PD, D1, D2, DT, T1, T2 and T3). M. Duval et

31

al [28, 29] narrated DGA analysis with IEC/IEEE codes for interpretation, sampling

procedure, extraction of gases and fault evaluation based on relative concentrations. Their

other publication [30] suggested different acceptable gas-in- oil levels in generation and

distribution transformers to the 90% & 95% respectively of the values given in IEEE

guide (1978). The advantages of metal deactivators [31] can effectively increase the

thermal resistance of mineral transformer oils but keep the electrical properties

unchanged for longer period and also reduces its charging tendency. Duval et al [32]

introduced the influence of paper insulation in acceptable gas-in-oil levels in DGA

analysis of transformers which involves the contents of carbon oxide and carbon

monoxide in oil. Duval et al [33] emphasis the popularity of interpretations of transformer

faults using new IEC publication 605999, 1999 contains the description of five main

faults and revision of IEC TC 10 databases for changed typical concentration values,

alarm values and rate of gas increase with previous IEC publication 599 issues.. Duval

[34] came out with a complete review of faults detectable by through DGA in

transformers on to improved Duval triangle 1 method for simulated samples for thermal

faults (T1, T2, T3), low-energy discharges (D1), PDs of the corona type, high-energy

discharges (D2) and separate DGA corresponds to a discharges of low- energy, which

will alter their normal gas composition specially the relative percentage of C2H4. M.

Duval again shown some new techniques for dissolved gas analysis in transformers [35].

Duval et al [36] suggested improving the reliability of transformer DGA analysis by

adding proper allowances to the laboratory inaccuracies. This helps to improve

transformer condition assessment and diagnosis by DGA of power transformers. M.

Duval presented a review [37] of previous researches in a conference on technical paper

in a CIGRE‘s report in 2006. For calculation of DGA limit values and sampling intervals

[38], has suggested the authorities and utilities to allow calculation of their own specific

DGA gas limits and required sampling intervals as a function of gas concentration and

also the rates of gas increasing during service. M. Duval [39] proposed new versions of

DTM 1 to DTM5 for all types of oil filled transformers may be filled with mineral/non-

mineral oils, OLTC oil analysis, low-temperature faults and low-energy faults.

2.6.3 Standards and Guides for Fault Interpretations

Various standards and guides are available for interpretation of faults of power

transformers through DGA analysis. These are adopted by the organizations/countries

according to their geographical, environmental conditions, need based, etc. CIGRE Group

2000 issued its guidelines for life management of power transformers [40]. IEEE

32

Standards [40, 41] are used as guide for interpretation of faults in power transformers

through DGA analysis. IEC publications [42-45] are said to be service guide to the

interpretation of dissolved and free gases analysis, which kept on amending the guides

time to time and need basis. IS- 10593 is also available for fault analysis for gas-in-oil

filled electrical equipments in service [46]. Other standards and guides [47-49] are used

as American standards, instructions, facilities, and techniques to help in fault diagnosis of

power transformers. R. R. Roger [50] used gas ratios to establish faults from extracted

gases and only by the use of IEEE and IEC codes for incipient fault interpretation.

2.7 ARTIFICIAL INTELLIGENCE (AI) APPLICATION IN DGA OF POWER

TRANSFORMERS

Computers applications have found wide spread applications ‗human-like abilities‘,

capabilities to make judgments, guesses, change of opinions in fault diagnosis of power

transformers in last two decades. Computer has reduced vagueness, uncertainties, analysis

times, but increased accuracy quicker remedial actions during off-line and on-line fault

diagnostic on power transformers. Data of the dissolved gases in oil insulation of a power

transformer can be incorporated into expert systems to facilitate decision making for fault

diagnosis. Due to the diverse gas content of transformer oil, computer based Artificial

Intelligence (AI) techniques and expert systems have been applied by the various

researchers, scientists, different organizations and utilities. Various AI techniques applied

by researchers for DGA in power transformers are as follows:

The fuzzy logic [51-65] analysis consists of three parts: fuzzification, fuzzy inference and

defuzzification. Fuzzification is the process of transforming crisp input values into grades

of membership for linguistic terms of fuzzy sets. The membership function is used to

associate a grade to each linguistic term. A chosen fuzzy inference system (FIS) is

responsible for drawing conclusions from the knowledge-based fuzzy rule set of if-then

linguistic statements. Fault types are first listed and then form the fuzzy rule set for the

diagnosis system. Defuzzification then converts the fuzzy output values back into crisp

output actions.

Artificial Neural Networks [66-76] is a massively parallel distributed processor, having a

natural tendency to acquire sufficient experimental knowledge and making it available for

use. Among these AI methods, the ANN is widely designed to diagnose transformer

faults. An important advantage of fault diagnosis based on the ANN is that it can learn

directly from the training samples and update its knowledge when necessary. The high

nonlinear mapping capability of neurons always provides a comparable and superior

33

performance over a fuzzy system solution. However, although the computational

complexity of the ANN is not too high, especially in the fault diagnostic process, it

involves certain problems including slow convergence, oscillation and so on, all of which

must be resolved before it is practically applied.

Albus proposed a neural model called CMAC (Cerebellar Model Articulation controller)

[77], which like the models of human memory, perform a reflexive processing. The

CMAC, in a table look-up fashion, produced a vector output in response to a state vector

input. Figure 3 shows a basic configuration of CMAC network for Key gas ratio. Refer to

the IEC std. 60599 the key gas ratio of C2H2/C2H4, CH4/H2, and C2H4/C2H6 are used as

the input states. The diagnosis system contains 9 parallel memory layers and every

memory layer has one output node. Every memory layer remembers one fault type

feature.

Bin et al [78] develop an assistance tool, the diagnostic algorithm must be easy to

implement in the portable device and hardware device with a compact configuration. The

grey theory provides the applications of clustering analysis is useful tool in fault

diagnosis, relational analysis, predication, and decision for the grey system. The so-called

‗‗grey‖ means that system information is incomplete, unclear, and uncertain. It is a useful

method to deal with the problems of limited, deficient, and or no rules available for data

processing. Its analysis makes use of minor data and does not demand strict statistical

procedures and inference rules. Lin et al [79] utilized GCA for fault analysis as a

function of mathematical operation for processing numerical data without adjusting any

parameter.

Due to incompleteness and complexity of condition estimate for power transformer, a

specific model based on rough set (ST) theory is presented. After the statistic analysis on

the collected fault examples of oil-immersed power transformer, Zhang et al [80] using

rough set theory to reduce result, estimate rules are acquired and they could be used to

improve the condition assessment of power transformer. The condition estimate inference

model was built based on the advantage of effectively simple decision rules and easy

reality of rough sets. Huang et al [81] shown significant advantage of the new method is

that it can discriminate the indispensable alarm signals from dispensable ones that would

not affect the correctness of the estimate results even if they are missing or erroneous.

In last two decades, some more AI techniques have been proposed by researchers in

experimental stages. Tang et al [82] presented Parzen–window (PW) based classifier for

fault diagnosis of transformers, which is able to interpret transformer DGA analysis with

34

a probabilistic scheme on the evidence of various gas ratios. [83, 84] have used wavelet

networks with NN and FL to compare and analyzing the network training process on

showing the accuracy and efficiency in fault diagnostics. Yang et al [85] a new self-

organizing polynomial networks (SOPNS) modeling technique is proposed and

implemented to serve as an intelligent decisions support for the transformer fault

diagnosis in comparison to conventional DGA and ANNs. Thang et al [86] illustrated a

novel approach for analysis and interpretation of DGA data which is more reliable

condition monitoring of power transformer based on self –organizing map (SOM)

algorithm. Liu et al introduced support vector machine (SVM), machine learning

algorithm based on statistical learning theory for fault diagnosis in power transformer

along with fault signature [87]. Jeong et al proposed a fault diagnosis on distribution

transformer using graphical information of frequency responses [88] for a group of

transformers for similar rating and capacities. [89-92] are some practiced artificial

intelligence method for DGA of power transformer into experimental stages to introduce

as fault diagnosis tool. Review of intelligent diagnostic method [93-97] have been

presented on conventional DGA method such as IEEE/IEC standards/codes, Roger‘s or

Dornenburg‘s ratios used for fault diagnosis of power transformers and others oil filled

electrical equipments.

2.8 ADVANCEMENTS IN FAULT DIAGNOSIS METHODS OF POWER

TRANSFORMERS

There are many advanced fault diagnosis methods are coming in the market and few of

them are still in the testing stages. Some of them are as follows:

2.8.1 Thermography

Infrared emission testing is used to check the external surface temperature of the

transformer on-line. It is useful for detecting thermal problems in a transformer, such as

cooling system blockages, locating electrical connection problems, and for locating hot

spots by Duke [98]. Infrared imagers ―see‖ the surface heat radiation from objects. It

cannot look ―inside‖ the transformer tank. Black and white/colourful thermograms (heat

pictures) show hot areas in white and cold areas in black. For colour thermograms, white

and red areas are usually hotter, while black and blue areas are colder. Infrared

thermography provides the heating patterns for the load that was on the equipment at the

time that the scan was performed. Any abnormal conditions can be located from the scan.

35

2.8.2 Sweep Frequency Response Analysis (SFRA/FRA)

In the sweep frequency response analysis test, the transformer is isolated from the system

and the impedance or admittance of the transformer is measured as a function of

frequency (typically to at least 2 MHz). This gives a ―fingerprint‖ of the transformer. The

test is repeated over time and the ―fingerprints‖ from two or more tests are compared.

There are two different test methods commonly used to carry out the FRA test; the swept

frequency test and the pulse test. The swept frequency method applies a variable

frequency voltage or a white noise voltage to the high-voltage winding and records the

response in another winding or terminal. This technique is more widely used in Europe

than in North America. This technique is picking up pace in India slowly. With this

technique a pulse signal is applied to the high-voltage winding, and the response is

recorded in another winding or terminal. In general, the greater the difference between the

two ―signatures‖, the greater movement in the transformer winding. The test requires

experienced personnel to compare the two signatures and evaluate the severity of the

movement by Singh J. at al [99-100].

2.8.3 Thermal UV- Response Test on Transformer Oil

U.V. visible spectrophotometer is used to analyse the transmission and absorption

capability of oil. As per IS, IEC, ASTM standards refractive property analysis is not a

mandatory test for liquid dielectrics, as initiative work absorption capabilities of

transformer oil is analysed. Experiment is carried out by UV spectrometer [17] having

single beam facility. The test is conducted for various hours of heating of pure

transformer oil. Based on the test results like wavelength and Peak absorption it is

possible to determine the level of thermal degradation of oil as an indirect methodology.

The wave length of pure transformer oil lies in the UV region (200 nm-300 nm).

2.8.4 Fault Detection (winding deformation) by Transformer Tank Vibration

Garcia et al [101-102], a model is developed for a transformer monitoring system to

estimate transformer tank vibration to detect faults. The model calculated vibration on

transformer tank started from some input variables that can be easily measured on the

transformer. In case of winding deformation winding vibration and, consequently, that of

the tank, changes and a big difference between the estimated and measured vibration

appear. The models were tested experimentally on a test transformer fitted with internal

and external accelerometers. The deformations were provoked in transformer windings to

test the model ability to detect. This detection method was experimental verification of

the proposed model that calculated vibration on the transformer tank.

36

2.8.5 Alternative Insulating Fluids and their DGA

In the present scenario, there has been an increase in the use of environmental-friendly

synthetic fluids (non-mineral oils) such as Silicone, Midel, FR3, Biotemp, `synthetic

esters, and natural esters. Although these non-mineral oils are environmental friendly that

results from their use with no compromise on safety and reliability. These synthetic fluids

are biodegradable unlike mineral oil. In order to their environmental performance, esters

higher flash point and fire point. The esters have a lower relative humidity than mineral

oil, this means that moisture has less of an impact on the dielectric strength of the esters

then mineral oils. When esters are used in conjunction with kraft papers and press boards,

cellulose is kept in a drier condition and the rate of cellulose degrades slower then

mineral oil [103-104]. Most of the conventional diagnostic methods (key gas ratios,

Roger, ratios, Doernenburg‘s ratios) are suitable for mineral oils filled in transformer

tanks but not suitable for the DGA diagnostics to equipment filled with non-mineral oils

(natural and synthetic esters, silicones). M. Duval conducted DGA tests separately on

these fluids and gave different Duval triangles 3 for each type of synthetic and natural

fluids (silicones, midel, FR3 and biotemp) [39].

Different proposal for improvement in transformer diagnosis using DGA might be fault in

order new transformers as laboratory test for routine condition monitoring are proposed

[105-108] literature. Petterson et al [109] tried to estimate the remaining life of power

transformers with insulations degradation during the service. Radigk et al [110]

contributed towards explaining problem of DGA and its evaluation. This also has

recommended how to analysis an oil sample and how to evaluate the results with the

focus on different equipments. [111] I A R Gray, Transformer chemistry service issued a

guide for transformer fault analysis through insulation degradations. Digiorgio et al [112]

issued through NTT (Northern Technology & Testing) Technical Bulletin for dissolved

gas standards/procedures of DGA used in different countries in the world. CIGRE

surveys [113-114] conducted in 2008 and 2007 for showing the influences of various

factors during the sampling process and its preservation before dissolved gas analysis are

carried out in laboratory. If these factors are not taken care-off, there likely to be errors in

interpretations for during fault analysis in transformers.

Literature survey on the proposed topic yields the important outcomes as follows;

i. There is need for an appropriate, efficient and cost effective non-invasive condition

monitoring and fault diagnosis method to test and detect faults at an early stage.

37

ii. Most of the faults and failures in transformers occur because of weak liquid or/and

solid insulation. Thus, it is desired to test transformer and transformer oil regularly to

detect fault at an early stage.

iii. Fault diagnosis methods of power transformers need to be compared for exact

identification of faults.

iv. It is observed that little work has been reported on the applications of Duval triangle

methods (DTMs) for fault diagnosis of power transformers.

v. It is also noticed that applications of fuzzy logic and artificial neural network have

not been reported so far on the applications of DTMs for fault diagnosis of power

transformers.

38

CHAPTER 3

TRANSFORMER INSULATING MATERIALS AND OIL TESTING

3.1 INTRODUCTION

It has been found that most of transformers fail during the service because of the

insulation failures chiefly because either liquid or solid insulating materials. Moreover, it

has further analyzed that many transformers get damaged due to transformer oil failure

than solid insulation failures. In this chapter main emphasis is given for quality and the

periodic monitoring of transformer oil. Typical transformer oil (mineral oil) specifications

are given in 3.1. All possible causes for weakening the insulation properties along with its

periodic testing/monitoring during service have been discussed. Traditional and non-

traditional testing of oil in the laboratories are performed to detect incoming fault

warnings at early stage. Thermal degradation of transformer oil at 1000C for 5, 7, 10, 15,

20, 24, 30 hrs are conducted and variations on some of the critical electrical

characteristics: i.e. BDV, IFT, water content, flash point, resistivity, tan-delta, loss factor,

dissolved gas analysis (DGA), degree of polymerization (DP), etc are observed at Central

power Research Institute (CPRI), NOIDA transformer oil testing laboratory. These tests

are performed on pure oil, mixed with kraft paper and press board and mixed with water.

The variations in critical electrical characteristics for these three cases have also shown.

3.2 INSULATING MATERIALS

These are the materials, which permit only a negligible current (order of pA) to flow in

phase with the applied voltage. Insulating materials or insulants are extremely diverse in

origin and properties. They are essentially non-metallic, organic or inorganic; uniform or

heterogeneous in composition; natural or synthetic. Many of them are of natural origin as,

for example, paper, cloth, paraffin wax and natural resins. Wide use is made of many

inorganic insulating materials such as glass, ceramics and mica. Many of the insulating

materials are man-made products manufactured in the form of resins, glass, ceramics, etc.

In the recent years wide use is made of new materials whose composition and properties

place them in an intermediate position between inorganic and organic substances. These

are the synthetic organo-silicon compounds, generally termed as silicones.

3.3 PROPERTIES OF INSULATING MATERIAL

There are many properties, which characterize the insulating materials, e.g. resistivity,

breakdown voltage, permittivity and dielectric loss, etc. An ideal insulating material

should have:

39

i. High dielectric strength sustained at elevated temperature

ii. High resistivity

iii. Good thermal conductivity

iv. High tensile and shear strength of solid insulation

v. High degree of thermal stability

In addition to the above properties, the material should have other mechanical properties

such as ability to withstand moisture (it should be non-hygroscopic), vibration, abrasion

and bending. Also, it should be able to withstand chemical attack, heat and other adverse

conditions of service.

3.4 TRANSFORMER PAPER INSULATION

Insulation grade paper is made by the delignification of wood pulp by the Kraft process. It

contains about 90% cellulose, 6-7% lignin and, the balance is hemi cellulose. The natural

humidity of paper is 4-5% by weight and the insulation is dried after winding to less than

0.5%. The dried paper is impregnated with insulating oil, which increases its dielectric

strength and also serves to cool the windings. Table 3.1 represents the specification of

electrical grade paper. The major constituent of paper is cellulose, which is a natural

polymer of glucose. Power transformer conductor windings are insulated by paper

impregnated with insulating oil, which is expected to last the life of the transformer (25

years minimum at an operating temperature of 65-95°C). A typical 600 MVA transformer

contains 12 t of paper, 30-120 μm thick (density 0.7-0.8 g/cm3), and 45 t (40,000 lts) of

oil. Heat, water and oxygen degrade (depolymerize) the cellulose, reducing the polymer

molecular chain length and with it the mechanical strength of the material.

Table 3.1: Electric Grade Papers Specification as per IEC 554-3-5 1984

S.No. Properties Specifications

1. Substances (g/m2) 30.4-40.0 ± 10%

2. Apparent Density (g/cc) 0.80 ± 0.05

3. Thickness (Micron) 38-50 ± 10%

4.

Tensile Index (Nm/g)

Min MD

CD

93

34

5. Water Absorption (mm)

MD

CD

8

8

40

6. Tear Index (MNm2/g)

Min MD

CD

5

6

7. Electric Strength (In Air at 90 oC) Min.(kV/mm) 7.0

8. Ash Contents Max. ( %) 1.0

9. Air Permeability (µm/Pa.Sec.) 0.5-1.0

10. Conductivity of Aqueous Extract Max. (mS/m) 10.0

11. Moisture Contents Max. (%) 8.0

12. pH of Aqueous Extract 6-8

13. Heat Stability Max. % (mS/m) at Max.120oC, 168 Hrs.

a. Decrease in Burst Strength

b. Increase in Conductivity

20

23

Local mechanical failure could lead to short the turns or paper fragments or fibers in the

oil ducts and hence to dielectric or thermal failure of the insulation system. Changes in

the compliance and tension of the windings with time, may result in distortion and an

increased susceptibility to short-circuit force failure of the aged insulation or even of the

winding itself. Water is a product of ageing. Its presence in the insulation increases

conductivity and the likelihood of gas bubble formation, reducing the thermal stability of

the insulation system during overload conditions.

3.5 TRANSFORMER INSULATING OIL

Oil used for insulation in transformers is mineral oil and it is obtained by refining crude

petroleum. Animal oils and vegetable oils are not used for this purpose as these form fatty

acids on heating which are corrosive for the cellulose paper used in insulation. Mineral

oils were in use as liquid dielectrics in electrical equipment for over hundred years now.

Despite the availability of a variety of synthetic oils, with far more superior properties,

mineral oils held its way, due to their abundant availability and economy. Three

properties, which are fundamental to use of mineral oil as dielectric are:

i. High insulating property,

ii. Good oxidative and ageing stability,

iii. Good heat transfer ability.

41

The reliable performance of mineral insulating oil in an insulation system depends upon

certain basic oil characteristics, which can affect the overall performance of the electrical

equipment [6]:

i. High electric strength to withstand the stresses in service

ii. Sufficiently low viscosity so that its ability to circulate and transfer heat is not

impaired

iii. Adequate low temperature properties down to the extreme of climatic conditions

expected at the installation site

iv. Proper oxidation resistance to ensure long life in service

v. Good resistance to emulsion to prevent holding up ofwater in suspension in it

vi.Free from inorganic acid, alkali and corrosive sulphur which causes corrosion of metal

parts and insulation and accelerate the production of sludge

vii. Free from sludging under normal separating conditions

viii. High flash/fire points

ix. Low pour point

x. Low water content

3.6 INSULATING OIL QUALITY

The condition of the oil greatly affects the performance and the service life of

transformers. A combination of electrical, physical and chemical tests is performed to

measure the change in the electrical properties, extent of contamination, and the degree of

deterioration in the insulating oil. The results are used to establish preventive

maintenance procedures, to avoid costly shutdowns and premature equipment failure, and

extend the service life of the equipment.

There is a multitude of tests available for insulating oil. Threshold levels for these tests

are specified in ASTM D3487 for new oils and IEEE Guide 637-1985 for service oils. As

paper degrades, a number of specific furanic compounds are produced and dissolved in

the oil. The presence of these compounds is related to the strength of the paper as

measured by its degree of polymerization (DP). Furan and phenol measurement in oil is a

convenient, noninvasive method to assess the condition of the paper insulation.

Transformer oil samples should be analyzed for furans and phenols when one or more of

the following conditions exist:

i. Overheating or overloading of the transformer,

ii. High levels of carbon monoxide or carbon dioxide,

42

iii. Rapid decrease of interfacial tension without a corresponding increase in acid

number,

iv. Sudden darkening of the oil

v. Sudden increase of the moisture content of the oil, transformers over 25 years old.

3.7 FACTORS AFFECTING INSULATING OIL QUALITY

There are various factors which are responsible to affect the quality of the insulating oil

filled in power transformers [112, 115]. These are as follows:

3.7.1 Effect of Oxygen on Oil

Moisture contamination is one of the most common causes of deterioration in the

insulating quality of oil. This contamination can be eliminated by purification. Less rapid

but more serious deterioration, the formation of acids and sludge is caused by oxidation.

Thus, the exclusion of oxygen is of prime importance. In open-breather transformers, the

oxygen supply is virtually unlimited and oxidative deterioration is consequently faster

than in sealed transformers. Atmospheric oxygen is not the only source of oxygen

available for the oxidation of insulating oils; water also serves as a source of oxygen and;

therefore, leaky gaskets increase the rate of oxidation and moisture contamination. The

rate of oxidation also depends on the temperature of the oil; the higher the temperature,

the faster the oxidative breakdown. This fact points to the importance of avoiding

overloading of transformers, especially in the summer time.

3.7.2 Moisture in Oil

Water can be present in oil (a) in a dissolved form, (b) as tiny droplets mixed with the oil

(emulsion), or (c) in a free state at the bottom of the container holding the oil.

Dissolved water is not removed by centrifugal treatment; the filtration process can

partially remove dissolved water if the filter papers are thoroughly dried before filtration

and are replaced frequently. The dissolved water is best removed by complete treatment

of the oil (oil reclamation process). Emulsification occurs when the tiny droplets unite to

form larger drops, which sink to the bottom and form a pool of free water. Emulsified

water or water in the Free State may be readily removed by filtering or centrifugal

treatment. The effect of moisture on the insulating properties of oil depends upon the

form in which the moisture exists. A very small amount of emulsified water has a marked

influence in reducing dielectric strength of oil shown in Fig. 3.1 whereas dissolved water

has little or no effect on the dielectric strength.

43

Water-ppm by volume

Figure 3.1: Dielectric strength Vs Water in oil

The amount of moisture, which can be dissolved in oil, increases rapidly as the oil

temperature increases Fig. 3.2. Therefore, insulating oil purified at too high a temperature

may lose a large percentage of its dielectric strength on cooling because the dissolved

moisture is then changed to an emulsion.

Figure 3.2: Water solubility Vs Temperature

In transformers, sludge sticks to the surfaces through which heat is dissipated; the sludge

forms a barrier to the flow of heat from the oil to the coolant and from the core and coils

to the oil. If allowed to continue, the sludge may block off the flow of oil through the

cooling ducts. As a result, the transformer insulating oil becomes hotter and the

transformer could be damaged, particularly between the coil turns of the windings.

Deterioration of the coil-turn insulation may eventually lead to short circuits between coil

turns and the breakdown of the transformer. When oxidation progresses to the point

where sludge is being precipitated, the first step should be to remove the sludge from the

transformer by a high-pressure stream of oil and to either replace the sledged oil or treat it

44

with activated clay to remove the acid and sludge precursors. Complete treatment of the

oil is normally less costly than replacing it with new oil.

3.7.3 Absorption of Moisture by Insulating Materials

Solid insulation in transformers is very porous and absorbs much water. Some of the

water that is dissolved in the oil is absorbed from the oil by the insulation. As more water

is dissolved in the oil, more water is absorbed by the insulation. Once water is absorbed

by insulation, it is difficult to remove. The most effective method for drying out the

insulation in transformers is with heat and vacuum. Sometimes a vacuum cannot be

applied in the field. In this case, the transformer insulation must be dried by circulation of

hot, dry oil. This oil should then be cooled and dried. Since the dielectric strength of

insulation is reduced by moisture, it is important that steps are taken to prevent the

introduction of moisture.

3.7.4 Absorption of Nitrogen by Oil

Special precaution should be taken when operating nitrogen-blanketed transformers to

avoid bubbling of the oil, due to the release of dissolved nitrogen when the pressure

drops. Experience has shown that the automatic gas-pressure regulating system should be

adjusted to limit the nitrogen pressure range from +3.4 to +21-kPa (+ 1/2- to + 3-lb/in2)

gauge to avoid formation of these bubbles and subsequent troubles due to corona

deterioration.

3.8 IMPORTANT OIL SPECIFICATIONS

The main requirements of transformer oil as listed in various national and international

standards only state the minimum requirements for transformer oil as shown in Table 3.2

whereas many transformer producers and electricity companies and boards have their

own stringent specifications based on the required conditions.

With advanced refining techniques, it is possible to produce transformer oil tailor-made to

suit the exact requirements for such specification.

Table 3.2: Characteristics requirement of IS, IEC, and BS specification for uninhibited

Transformer Oil

S.No. Characteristics IS-335

(1993)

IEC-296

(Class I)

IEC-II

(Class)

BS-148

(Class I)

1. Appearance The oil shall be clear, Transparent & free from suspended

matter or sediments.

2. Density (g/cm2) 0.89 at 0.895 at 0.89 5at 0.895 at

45

29.5oC(max) 20

oC(max) 20

oC(max) 20

oC(max)

3. Kinematic

Viscosity (cSt)

27 max at 270C 16.5 max at

400C 800

max at-150C

11.0 max at

400C

16.5 max at

400C 800

max at 150C

4. Flash Point oC

(min)

140 140 130 140

5. Pour Point, 0C

(max)

-6 -30 -45 -30

6. Interfacial

Tension N/m

(min)

0.04 - - -

7. Electric Strength

(BDV), kV

i.New Unfiltered

oil

ii.After filtration

30(min)

60 (min)

30 (min)

50 (min)

30 (min)

50 (min)

30 (min)(As

delivered)

8. Tan Delta at 900C 0.002 (max) 0.005 (max) 0.005 (max) 0.005 (max)

9. Resistivity (Ω m)

at

900C

270C

35X1012

(min)

1500X1012

(min)

- - -

10. Oxidation

Stability 164 hrs.

1 Neutralization

value mgKOH/g

2 Sludge content

%by wt

0.4 (max)

0.1 (max)

0.4 (max)

0.1 (max)

0.4 (max)

0.1 (max)

1.5 (max)

1.0 (max)

11. Neutralization

Value

1 Total acidity

mgKOH/g

2 Sludge content

% by wt.

0.03

Nil

0.03

-

0.03

-

0.03

-

46

12. Corrosive

Sulphur

Noncorrosive Noncorrosive Noncorrosive Noncorrosi

ve

13. Water content

(ppm)

50 (max) 40 (max) 40 (max) -

14. Oxidation

Inhibitor

0.05%max. is

treated as

absence of

inhibitor

Non

detectable

Non

detectable

Non

detectable

15. S.K. Value Under

considerations

- - -

16. Accelerated

Ageing Test

(open beaker

method with

copper catalyst)

i. Resistivity at

270C

ii.Resistivity at

900C

iii.Tan Delta at

900C

iv.Total acidity

mgKOH/g

v.Total sludge

25x1012

Ωcm

0.2x1012

Ωcm

0.2 (max.)

0.05 (max.)

0.05% by

wt(max.)

-

-

-

17. Gassing

Tendancy

- - - +5 (max.)

3.9 TESTING OF TRANSFORMER OIL

In order to maintain stability in the electrical system, condition monitoring of the

sensitivity equipments in power system is very essential. Among those equipments in

which higher level of concentration required is power transformer. Power transformers

are the most critical components of power system, in which mineral oil plays an

47

important role of insulation and moreover of its thermal properties it acts as a cooling

medium. Transformer life time gets reduced because of deterioration of insulation, which

may be solid or liquid insulation. To determine the performance of transformer oil it is

necessary to have investigations on ageing due to thermal and water content.

Experiments are conducted as per American Society of Testing and Materials (ASTM),

Indian standards (IS) and International Electrotechnical Committee (IEC) standards.

Various standards adopted for testing of each parameter is given in the Table 3.3.

Table 3.3: Standards and testing

S. No. Parameters Testing standards adopted

1 Acidity IS 6792

2 Water Content IEC 814

3 BDV IS6 792

4 Volume Resistivity IEC 60247

5 Tan-delta/Loss Factor IEC 60247

6 Dielectric constant IEC 60247

7 Flash Point ASTM D 3828

Basically to determine the thermal degradation, different tests can be conducted as per the

transformer construction and types of liquid and solid insulations are used. Those are

listed as follows:

i. Thermal Degradation test on transformer oil.

ii. Thermal Degradation test on transformer oil along with Press board and paper.

iii. Degradation test due to accumulation of water content on transformer oil.

Five basic tests on insulating oil, when considered collectively, give a reasonably accurate

diagnosis with respect to the serviceability of insulating oil. The tests are:

i. Acidity

ii. Water content

iii. Break down voltage (BDV)

iv. Volume resistivity

v. Tan-delta

vi. Dielectric constant

vii. Flash point

48

Although these oil tests evaluate the insulating characteristics of the oil, the information

gained from the analysis of these tests, coupled with DGA and DP provide excellent

diagnostic tools for monitoring and evaluating the operating conditions of the oil-filled

equipment.

Samples of insulating oil are used for different thermal ageing tests, with presence of

press boards, Kraft papers and water contents. Oil characteristics are analysed to find the

degradation and to correlate with types of faults. Various trends changes in the critical

characteristics of oil, interfacial tension (IFT) and acid number (NN) of oil were also

examined. Transformer oil testing for fault diagnosis is divided in two categories:

3.9.1 Non Traditional Tests on Transformer Oil

Some of them are as follows:

i. Acidity (Acid number—NN)

Acidity is a measure of free organic and inorganic acid present in the oil and expressed in

terms of milligrams of Potassium Hydroxide (KOH) required to neutralize the total free

acids [18] in one gram of oil. Presence of these acid contents in oil causes detrimental to

insulation system and leads to corrosion of tank. Acids usually originate from oil

decomposition, oxidation products and external atmospheric sources. Acidity level of

transformer oil is measured as per British Standard BS-2000 Part1 [18]. The acid attracts

metal inside the tank and form soaps (sludge). Sludging has been found to begin when

NN reaches 0.40; it is obvious that he oil should be reclaimed long before it reaches

0.40.it is recommended that oil be reclaimed when NN reaches 0.20 mg KOH/gm

ii. Water Content (Test Method IEC 814)

Water in minute quantity is harmful in power equipments because it is attracted to the

places of greatest electrical stress and this is where it is most dangerous. Water accelerate

the deterioration of both the insulating oil and paper insulations, librating more water in

the process (heat catalyze).

This is never ending circle and once a paper insulation has been degraded (loss of

mechanical strength) it can never (unlike the oil) be returned to its original condition.

Internal sources:- paper degradation produces water, oil degradation produces water, wet

insulation contaminates the oil (temperature dependent).

iii. Break down voltage (BDV)

Break down voltage is the ability of oil to withstand stresses without failure. Breakdown

strength analysis of transformer oil gives effective results through which suitable

dielectric material for the related high voltage applications can be explored. The

49

Breakdown Voltage (BDV) test kit consists of two electrodes mounted on horizontal axis

with 2.5 mm gap and enclosed in a glass chamber. Electrodes used in test apparatus are of

sphere-sphere electrode configuration. The kit is provided with standard oil test set up is

powered through a step up transformer capable of offering up to 60 kV. The tests are

carried as per Indian standard IS 6792 [16] specifications. Test result of BDV is taken an

average of at least of 5 readings.

iv. Tan-delta, Resistivity and Dielectric Constant

These tests can be performed on the measurement test kit consists of three terminal oil

cell with heating chamber for oil cell. The tests are carried out as per IEC 60247 Standard

[20].

v. Flash Point

Flash point of a volatile liquid is the lowest temperature at which it can vaporize to form

an ignitable mixture in air. The Fire point is defined as the temperature at which the

vapour continues to burn after being ignited. It is the lowest temperature at which, on

further heating beyond the flash point, the sample support and combustion for five

seconds. Flash and fire point test is carried out in Pensky-Martens open cup apparatus as

per ASTM D 3828 [17].

vi. Loss Factor

Loss factor is a measure of imperfection of dielectric nature of oil. For a perfect dielectric

when applied with a sinusoidal ac voltage, the current flowing through it should lead the

voltage by 900. But it is not in the case of reality, the angle by which it is short of 90

0 is

called the loss angle. The Loss tangent is also known as dielectric dissipation factor,

which measures the leakage current through oil. It is the measure of contamination

products like moisture and oxidation products. Dielectric dissipation value should be low

for good transformer Oil. Resistivity is the most sensitive property of oil requiring highest

care for its proper determination. Resistivity in ohm cm is numerically equivalent to the

resistance between opposite faces of a centimeter cube of the liquid. Insulation resistance

of windings of a transformer is also dependent upon the resistivity of oil. A low value

indicates the presence of moisture and conductive contaminants.

vii. Viscosity

Viscosity of oil is the measure of the transformer oil resistance to shear. Viscosity is more

commonly known as resistance to flow for fluids. If lubricating oil is considered as a

series of fluid layers superimposed on each other, the viscosity of the oil is a

measurement of the resistance to flow between the individual layers. A high viscosity

50

implies high resistances to flow while a low viscosity indicates a low resistance to flow.

Viscosity varies inversely with temperature. Viscosity is measured using Red wood

viscometer at the temperature of 300 0C. The test is carried out as per ASTM D 2162-06,

IP 70 [19].

viii. Interferential Tension (IFT)

The IFT measures the tension at the interface between two liquids (oil and water). Which

do not mix and is expressed i.e. dyne/cm the test is sensitive to the presence of oil decay

product & soluble polar contaminants from solid insulating materials. Good oil will have

an IFT of between 40 -50 dynes /cm. Oil oxidation products lower the IFT and have an

affinity for both water (hydrophilic) and oil. This affinity for both substances lowers the

IFT. The greater the concentration of contaminants the lower the IFT, with a badly

deteriorated oil having an oft of 18 dynes/cm or less. IFT is carried out as per standards

ASTM – D 971.

3.9.2 Traditional Tests for Fault Diagnosis

These tests are used for the fault diagnosis of transformers during service as well to find

the reasons of failures, like a postmortem carried out on human body. These are:

i. Dissolved gas analysis (DGA)

Dissolved Gas Analysis is the most important tool in determining the condition of a

transformer. It is the first indicator of a problem and can identify deteriorating insulation

and oil, overheating hot spot, parallel discharge, and arcing. The health of the oil is

reflective of the health of the transformer itself. DGA consist of sending transformer oil

samples to a commercial laboratory for testing. The most important indicators are the

individual and total dissolved combustible gases (TDCG) and total combustible gases

(TCG) generation rates based on IEC 60599-1999 and IEEE C- 57-104 TM

2008.

ii. Degree of polymerization (DP)-( Standards ASTM D 5837-1996)

One of the most dependable means of determining paper deterioration and remaining life

is the degree of polymerization (DP) test of the cellulose. The cellulose molecule is made

up of long chain of glucose ring which form the mechanical strength of the molecule of

the paper. DP is the average number of these rings in the molecule. As paper ages or

deteriorates from heat, acids, oxygen on water, the number of these rings decrease .when

the insulation is new, the DP is typically between 1,000-1,400, reaches around 200, the

insulation has reached the end of the life. All mechanical strength of the insulation has

been lost; the transformer must be replaced. When cellulose insulation decomposes due to

overheating chemicals in addition to CO2 and CO are released and dissolved in the oil,

51

these chemical compound are known as furanic compound or furans. The most important

one, for our purposes is 2-frfuraldehyde. On healthy transformer there are no detectable

furans in the oil, or they are less than 100 ppb. In case where significant damage to paper

insulation from heat has occurred, furans level has been found to be at least 100 ppb and

up to 70,000 ppb.

3.10 IFT-NN RELATIONSHIP

Studies have shown in Table 3.4 that a definite relationship exists between acid number

(NN) and Interfacial Tension (IFT) [111]. An increase in NN should normally be

followed by a drop in IFT. The IFT test is a powerful tool for determining how insulating

oil has performed and how much life is left in the oil before maintenance is required to

prevent sludge. The IFT provided an excellent back up test for the NN. IFT not

accompanied by a corresponding increase in NN indicates polar contamination which has

not come from normal oxidation.

Although a low IFT with a low NN is an unusual situation, it does occur because of

contamination such as solid insulation materials, compounds from leaky pot heads or

bushings, or from a source outside the transformer.

Table 3.4: Relationship between IFT Vs NN

CORRELATION BETWEEN IFT Vs NN IN OIL FILLED TRANSFORMERS

Neutralization Number Vs sludge

NN(mg/KOH/g) %(out of 500 transformers) Units Sludged

0.00-0.10 0 0

0.11-0.20 38 190

0.21-0.60 72 360

0.61 and above 100 500

Interfacial tension Vs Sludge

IFT (Dynes/cm) %(out of 500 transformers) Units Sludged

Below 14 100 500

14-16 85 425

16-18 69 345

18-20 35 175

20-22 33 165

22-24 30 150

Above 24 0 0

52

3.10 THERMAL DEGRADATION TEST ON PURE TRANSFORMER OIL

Permissible critical parameter specifications as per IS 1866-200 are shown in Table 3.5.

Table 3.5: Permissible limits as per IS 1866-2000

S. No Parameters Highest equipment voltage, KV

<72.5 72.5 to 170 >170

1 Electric Strength KV/mm, Min >30 >40 >50

2 Dielectric dissipation factor @ 90

oC, Max

1.0 1.0 0.2

3 Specific resistance, 90 oC 10

12

ohm.cm, Min

0.1 0.1 0.1

4 Water content, ppm, Max ≤50 ≤40 ≤20

5 Neutralization value, mgKOH/g,

Max

0.3 0.3 0.3

6 Interfacial tension mN/m, Min 15 15 15

7 Flash point, oC, Min Min 125

oC or Max decrease by 15

oC

8 Sediment & Sludge Nil Nil Nil

The Initial sample parameters for pure transformer oil are given in the Table 3.6.

Table 3.6: Initial samples parameters

No. Parameters Values

1 Acidity (mgKOH/g) 0.0269

2 Viscosity (centistoke) 20.06

3 Breakdown

Voltage (KV/2.5mm)

15

4 Flash point (0C) 160

5 Volume

Resistivity(ohm-cm)

339X109

6 Dielectric constant 1.08

7 Tan-delta(900C) 0.1444

Various changes in the critical characteristics like acidity BDV, flash point, loss factor,

volume resistivity, dielectric constant, and viscosity may be examined. Test results for

thermal degradation of oil are given in Table 3.7. Various parameters changes are noticed.

Acidity value slowly increase at the initial stage of heating up to 10 hours of heating, then

53

after 20 hours of heating it remains unchanged. It shows that acidity value after 30 hours

of heating was within the accepted level.

BDV gets increases at the initial stage; the reason is the moisture content in oil gets

evaporated at starting stage. On increasing the hours of heating it trends to decrease

rapidly. Flash point variation shows a steady level up to 18 hours of heating, then its

value gets sharp increase in 24 hours of heating, but finally the characteristics shows a

steady margin value of 160 0C to 161

0C. Fire point value characteristics shows a rapid

increase for 10 hours of heating and then it tends to lower, finally after 18 hours of

heating the value gets saturated in the range of 230 0C.

Table 3.7: Thermal degradation test on pure transformer oil

Sample

No of

Hrs of

heating

Total Acid

value

(mgKOH/g)

BDV

kV/mm

Flash

Point

(oC)

Volume

resistivity

X109

Loss

tangent

Dielectric

constant

1 5 0.02940 25 160 119 0.385 1.56

2 7 0.03205 33 160 452.36 0.117 0.56

3 10 0.03457 23 160 145.76 0.35 1.54

4 12 0.03478 25 160 123.77 0.36 1.49

5 14 0.03479 20 160 219.89 0.087 0,38

6 16 0.03478 19 160 201 0.265 0.76

7 18 0.0336 20 159 198 0.301 0.80

8 20 0.03501 18 160 195 0.372 0.81

9 24 0,03490 17 161 188 0.388 1.28

10 30 0.03491 16 160 180 0.396 1.51

Volume resistivity at initial stage shows a peak increase, but when the oil is heated the

value of volume resistivity trends to decrease. It is inferred that resistivity value of oil

reduces on days of heating. Dissipation factor value at initial stage shows a higher value

but on further heating the dissipation factor value gets decreased. Dielectric constant at

initial stage gets lowered and then it shows a steady increase.

3.12 THERMAL DEGRADATION TEST ON OIL WITH PRESS-BOARD AND

KRAFT PAPER

Another set of tests with press board and craft paper, all the test samples were heated up

to 5, 7, 9, 12, 15, 18, 20 Hours. Test is conducted to simulate the real time thermal

54

degradation of oil along with deterioration of solid insulation like press board and craft

paper. The total weight of press board and kraft paper mixed along with oil is of 300gm.

Maximum temperature maintained inside the apparatus is of 100 0C. Various changes in

the critical characteristics like BDV, Flash point, Fire point, Loss factor, Resistivity,

Dielectric constant, and Acidity were examined. Test results for Thermal Degradation test

on oil along with Press board and paper is given in Table 3.8.

Table 3.8: Thermal Degradation test on oil along with Press board and Kraft paper

Sample

No of

Hr of

heating

Total

Acid

value

BDV Flash

Point

Volume

resistivity

X109

Loss

tangent

Dielectric

constant

1 5 0.5142 17 160 506.39 0.078 0.33

2 7 0.8530 10 170 2242.23 0.008 0.02

3 9 0.7462 16 165 4002.14 0.006 0.01

4 12 0.7831 15 154 4000.58 0.005 0.01

5 15 0.8310 13 163 3780.00 0.008 0.02

6 18 0.8309 13 164 3500.27 0.008 0.02

7 20 0.8308 11 162 2289.91 0.009 0.01

Various observations are made from Table 3.8. Acidity value slowly increase at the initial

stage of heating up to 10 hours of heating, then after 20 hours of heating it remains

unchanged. It shows that acidity value after 30 hours of heating was within the accepted

level. Viscosity value shows a sharp increase at 7 hours of heating and then trends to

decrease down rapidly therefore flow rate of oil gets increased. BDV gets increases at the

initial stage the reason is the moisture content in oil gets evaporated at starting stage. On

increasing the hours of heating BDV trends to decrease rapidly. Flash point to hours of

heating. Flash point values at the initial stage gets sharp increase and then further heating

it will gets reduced. Fire point value shows increasing trend characteristics and more or

less it attains a saturated value after 15 hours. At the initial stage the peak absorption

value sharply increases up to 9 hours of heating and then it shows a gentle increase of

value. Transmission property of oil shows a variation of up and down values, at one stage

(18 hours) the value starts to decrease. Volume resistivity value obtained in the sample of

oil along with press board and craft paper is very high compared to characteristics value

of pure oil. Volume resistivity at initial stage shows a peak increase, but when the oil is

55

heated the value of volume resistivity trends to decrease. It is conferred that resistivity

value of oil reduces on days of heating. Loss factor value obtained in the sample of oil

along with press board and Kraft paper is very low compared to characteristics value of

pure oil. Dissipation factor value at initial stage shows a higher value but on further

heating the dissipation factor value gets decreased. Shows the variation of Dielectric

constant to hours of heating, the value slowly degrades on increase of hours of heating.

3.12 DEGRADATION TESTS DUE TO ACCUMULATION OF WATER

CONTENT

In this test all the test samples were mixed in the proportionate of 10%, 15%, 20%, and

25% of 700 ml of water. Trends of changes in the characteristics were analysed. Test is

conducted to simulate the characteristics changes due to availability of higher

concentration of water content (ml) in oil. Various changes in the critical characteristics

like BDV, Flash point, Fire point, UV response, Loss factor, Resistivity, Dielectric

constant, Acidity and Viscosity were examined. Test results for Degradation due to

accumulation of water content are given in Table 3.9.

Table 3.9: Influence of water content

Sample

Water

content

(ml)

Total

Acid

value

BDV Flash

Point

Fire

Point

Volume

resistivity

X109

Loss

tangent

Dielectric

constant

1 70 0.02944 9 180 200 Not obtain3ed because of water

contents 2 105 0.112705 16 Not obtained

because of

water

contents

3 140 0.16229 10

4 175 0.034735 4

Some of the observations are made from the test results given in Table 3.9. Acidity value

slowly increases on increase of water content, but whenever the level of water content

increases beyond certain percentage of limit the acidity value trends to decrease.

Viscosity value shows a sharp increase at 20 % of water content and then trends to

decrease down rapidly, therefore flow rate of oil gets increased. On increasing the water

content values the BDV value reduces also rapidly.

The following significant results are concluded from the oil parameter testing and

correlation between parameters test values as in Table 3.10.

56

Table 3.10: Significance of test

Sl. No. Test Method Significance

1. Interfacial

tension

Measures the

attraction force

between unlike

molecules.

Indicates polar contaminants

including deterioration products.

Index of deterioration/

contamination.

2. Flash point Measures the

minimum temperature

at which momentary

flash occurs

To find flammable mixture in

transformer oil.

3. Neutralisation

value

Measures Acidic

constituents in oil.

Acids are highly corrosive,

polymerize to form insoluble

sludge. Acids are undesirable.

4. Electric

strength

Measure of minimum

voltage at which oil

breaks.

Indicates free water, dirt, fiber or

conducting particles.

5. Dielectric

dissipation

factor or Tan-

delta

Measure of dielectric

loss in oil- energy

dissipated as heat.

Very sensitive to moisture,

conducting impurities

contaminates & products of

oxidation like acid.

6. Specific

resistance

Measure of resistance

per unit volume.

Sensitive to moisture,

contaminates & products of

oxidation like acid.

Gauge of Purity of oil.

7. Water content Measure the total

water present on oil.

Moisture cause metal corrosion

reduces electric strength &

increase the rate of deterioration

of oil / paper / press board.

8. Sludge

content

Measure the sludge

formed by

combination of

degradation products

of oxidation.

Sludge deposits on core, coil &

cooling ducts lower heat

dissipation & increases aging.

57

3.13 SUMMARY

The cost to repair and replace a power transformer is very high. The time taken to repair

or to replace transformer results in loss and inconvenience to the utilities. Since

transformer oil occupies maximum portion and performs various functions in the

transformers, its quality get deteriorated quickly by hydrolytic, oxidation and thermal

effects, which further degrades or weakens the dielectric property of the oil and stars

degrading the properties of sold insulation of the transformers.

Critical parameters of oil has been analyzed for various levels of thermal degradation

with combinations of insulation materials. Ageing characteristics analyses of transformer

oil using refractive property give some idea towards level of degradation. Correlations

among the electrical characteristics were examined and the results were inferred.

Effective condition monitoring of transformer oil surely paves the way for increased life

time of insulation, as a result catastrophic failure and forced outages shall be minimized.

58

CHAPTER 4

DISSOLVED GAS ANALYSIS FOR FAULT DIAGNOSIS

4.1 INTRODUCTION

It is has been established that transformer oil degrades by three factors; hydrolytic,

oxidative and thermal. Transformer oil is a long chain of hydro-carbon molecules. The

molecular chain breaks into smaller size particles under these three degrading factors.

Thus several gases are generated in transformer oil and keep dissolving in the oil. Access

gases get collected in Buckotz‘s relay of the transformers. Therefore, it is necessary to

monitor and diagnose the health condition of the transformer through dissolved gas

analysis periodically. This chapter describes the mechanism of gas formation due to

decomposition of oil, decomposition of cellulose insulation and various other sources of

gases in the transformer. It also involves the limits of the gases contents and associated

faults. Further sampling procedures along with effects of various factors affecting the

dissolved gas analysis (DGA) interpretations wrongly has been described. Some of the

recent conventionally used DGA analyzers are described briefly. Gas concentration levels

and rates of gas increase in oil (typical, alarm and pre-failure values) are indicated as a

function of time. The chapter also gives recommended safe values of various gases.

The present chapter classifies various faults which can be detected with the help of DGA.

Guidelines for interpretation of faults adopted in various countries and standards are

given in tables of this chapter. Different diagnostic standards/methods for interpretation

of dissolved gases such as IEEE standards ( Dornenburg‘s, Roger‘s and Key gas methods

), IEC ratio codes, Duval Triangle method are described. Most of the interpretation

techniques given in IEEE standards C-57.104TM

1997 & 2008, which are also basis for

the Bureau of Indian Standards (BIS) are illustrated to the large extant. This IEEE

standard is also more or less drawn from IEC 60599 codes. Methods used by other

countries for fault diagnosis are also listed with brief descriptions. Some case studies on

various transformers in different organizations show comparative analysis by Rogers ratio

and Duval triangle. Sampling intervals and action taken based on total combustible gases

(TCG) and total dissolved combustible gases TDCG are also given in the tables.

4.2 DECOMPOSITION OF OIL

Mineral Insulating oils are made of a blend of different hydrocarbon molecules containing

CH3, CH2 and CH chemical groups linked together by carbon-carbon molecular bonds.

Scission of some of the C-H and C-C bonds may occur as a result of electrical and

59

thermal faults, with the formation of small unstable fragments, in radical or ionic form,

such as H, CH3, CH2, CH OR C (among many other more complex forms), which

recombine rapidly, through complex reactions, into gas molecules such as hydrogen (H-

H), methane (CH4), ethane (C2H6), ethylene (C2H4) or acetylene (C2H2). C3 and C4

hydrocarbon gases, as well as solid particles of carbon and hydrocarbon polymers, are

other possible recombination products. The gases formed dissolve in oil, or accumulate as

free gases if produced rapidly in large quantities, and may be analyzed by DGA according

to IEC 60567.

Low energy faults such as partial discharges of the cold plasma type (corona discharges),

favour the scission of the weakest C-H bonds (338 kJ/mole) through ionization reactions

and the accumulation of hydrogen as the main recombination gas. More and more energy

and/or higher temperatures are needed for the scission of the C-C bonds and their

recombination into gases with a C-C single bond (607 kJ /mole), C=C double bond (720

kJ/mole) or CºC triple bond (960 kJ/mole), following processes bearing some similarities

with those observed in the petroleum oil-cracking industry.

4.3 FORMATION OF GASES IN TRANSFORMER OIL

Mineral oils (transformer oil) are composed of saturated hydrocarbons called paraffins,

whose general molecular formula is CnH2n+2 with n in the range of 20-40. The cellulosic

insulation material is a polymeric substance whose general molecular formula is

[C12H14(OH)6]n with n in the range of 300-750. Gases are formed inside an oil-filled

power transformer, in that various gases begin forming at specific temperatures [5] shown

in Fig. 4.1. Hydrogen and methane begin to form in small amounts around 150 °C. Notice

from the chart that beyond maximum points, methane (CH4), ethane and ethylene

production goes down as temperature increases. At about 250°C, production of ethane

(C2H6) starts. At about 350 °C, production of ethylene (C2H4) begins. Acetylene (C2H2)

starts between 500 °C and 700 °C. In the past, the presence of only trace amounts of

acetylene (C2H2) was considered to indicate a temperature of at least 700 °C had

occurred. But recent discoveries have led to the conclusion that a thermal fault (hot spot)

of 500 °C can produce trace amounts (a few ppm). Larger amounts of acetylene can only

be produced above 700 °C by internal arcing. Notice that between 200 °C and 300 °C, the

production of methane exceeds hydrogen. Starting about 275 °C and on up, the

production of ethane exceeds methane. At about 450°C, hydrogen production exceeds all

others until about 750 °C to 800 °C; then more acetylene is produced. It should be noted

that small amounts of H2, CH4, and CO are produced by normal aging. Thermal

60

decomposition of oil-impregnated cellulose produces CO, CO2, H2, CH4, and O2.

Decomposition of cellulose insulation begins at only about 100 °C or less. Therefore,

operation of transformers at not more than 90 °C is imperative. Faults will produce

internal ―hot spots‖ of far higher temperatures than these, and the resultant gases show up

in the DGA.

Figure 4.1: Gas generation chart [3]

4.4 SOLUBILITY OF GASES IN TRANSFORMER OIL

The solubilities of the fault gases in Transformer oil as well as their temperature

dependence are also important factors for consideration in fault gas analyses. It should be

noted that there are almost two orders of magnitude difference between the least soluble

(H2) and the most soluble (C2H2) gas. The majority of gases that are indicative of faults

are also those that are in general the more soluble in the oil. When the rates of gas

generation are being followed it is important to take into account the solubility of these

gases as a function of temperature (Fig. 4.2). Over a temperature range of 0-80oC some

gases increases in their solubility upto 79% while others decrease their solubility upto

66%.

61

Figure 4.2: Relative solubility (Y-Axis) as function of temperature OC (X-Axis)[112]

Oil may oxidize with the formation of small quantities of CO and CO2, which can

accumulate over long periods of time into more substantial amounts.

4.5 DECOMPOSITION OF CELLULOSE INSULATION

The polymeric chains of solid cellulose insulation (paper, pressboard, wood blocks)

contain a large number of an hydro glucose rings, and weak C-O molecular bonds and

glycosides bonds which are thermally less stable than the hydrocarbon bonds in oil, and

which decompose at lower temperatures. Significant rates of polymer chain scission occur

at temperature higher than 105°C, with complete decomposition and carbonization above

300°C. Mostly CO, CO2 as well as H2O are formed, in much larger quantities than by

oxidation of oil at the same temperature, together with minor amounts of hydrocarbon

gases and furanic compounds. The latter can be analyzed according to IEC 61198, and

used to complement DGA interpretation and confirm whether or not cellulosic insulation

is involved in a fault. CO and CO2 formation increases not only with temperature but also

with the oxygen content of oil and the moisture content of paper.

4.6 OTHER SOURCES OF GASES IN TRANSFORMERS

Gases may be generated in some cases not as a result of faults in the equipment but

through rusting or other chemical reactions involving steel, uncoated surfaces or

protective paints.

Hydrogen may be produced by reaction of steel with water, as long as oxygen is available

from the oil near by. Large quantities of hydrogen have thus been reported in some

transformers that had never been energized. Hydrogen may also be formed by reaction of

free water with special coatings on metal surfaces, or by catalytic reaction of some types

of stainless steel with oil, in particular oil containing dissolved oxygen at elevated

62

temperatures. Hydrogen may also be formed in new stainless steel, absorbed during its

manufacturing process, or produced by welding and released slowly into the oil.

Hydrogen may also be formed by the decomposition of thin oil film between overheated

core laminates at temperatures of 140°C and above. Gases may also be produced by

exposure of oil to sunlight or may be formed during repair of the equipment. Internal

transformer paints, such as alkyd resins and modified polyurethane‘s containing fatty

acids in their formulation, may also form gases.

These occurrences, however are very unusual, and can be detected by performing DGA

analyses on new equipment, which has never been energized, and by material

compatibility tests. The presence of hydrogen with the total absence of other hydrocarbon

gases, for example may be indication of such a problem.

In-service transformers always have some fault gases dissolved in their oil. Only when

these levels exceed some threshold value, a fault is suspected. Several recommended safe

values have been published. Limits as per IEC 60599 in power transformers are listed in

Table 4.1 and Table 4.2 for permissible range on ageing of transformers.

Table 4.1: Recommended safe value of various gases in [µl/L(ppm)]

Table 4.2: Permissible concentration of dissolved gases on ageing

Gas H2 CH4 C2H2 C2H4 C2H6 CO CO2

Violations Limits

Max.(ppm)

100 120 35 50 65 350 2500

Typical Ratio of Gas

Increased(ppm/day)

<5 <2 <0.1 <2 <2 <50 <200

Gas Less than 4 years in

service (ppm)

4-10 years in

service (ppm)

More than 10 years in

service (ppm)

Hydrogen 100-150 200-300 200-300

Methane 50-70 100-150 200-300

Acetylene 10-30 30-50 50-80

Ethylene 100-150 150-200 200-400

Ethane 30-50 100-150 150-300

Carbon Monoxide 200-300 400-500 550-700

Carbon Dioxide 3000-3500 3500-600 6000-12000

63

A list of key gases and their related and key faults are shown in Table 4.3. This has been

discussed in detail in IEEE Std. C-57, 104TM

-2008, 1991 ―IEEE Guide for the

Interpretation of Gases Generated in Oil-Immersed Transformers‖ [41, 45].

Table 4.3: Key gases generated by particular fault

4.7 DISSOLVED GAS ANALYSIS

Testing of the insulating oil is one of the most common tests used to evaluate the

condition of transformers in service. The Dissolve Gas Analysis (DGA) test is one of the

important diagnostic techniques for fault diagnosis in power transformers. Insulating oils

under abnormal electrical or thermal stresses break down to liberate small quantities of

gases. The composition of these gases is dependent upon the type of fault. By means of

dissolved gas analysis, it is possible to distinguish faults such as partial discharge

(corona), over-heating, and arcing in a great variety of oil filled equipment. A number of

samples must be taken over a period of time to discern trends and to determine the

severity and progression of incipient faults. The gases in oil tests commonly evaluate the

concentration of hydrogen, methane, acetylene, ethylene, ethane, carbon monoxide,

carbon dioxide, nitrogen, and oxygen.

The relative ratios and the amount of gas detected in the sample are used to detect

problems with the insulation structure.

Information from the analysis of gasses dissolved in insulating oil is one of the most

valuable tools in evaluating the health of a transformer and has become an integral part of

preventive maintenance programs. Data from DGA can provide:

i. Advanced warning of developing faults

ii. Monitoring the rate of fault development

iii. Confirm the presence of faults

iv. Means for conveniently scheduling repairs

v. Monitoring of condition during overload

Material Condition Key Gases

Oil Overheated >110 0C Methane (CH4), Ethane (C2H6),

Ethylene (C2H4), Organic Acids

Oil Electric Stress (Partial Discharge

and Arcing to 1000 0C)

Hydrogen (H2), Acetylene (C2H2),

Waxes and Water

Cellulose Overheated >150 0C Carbon Monoxide (CO), Carbon

Oxide (CO2), Water (H2O)

64

4.8 FAULTS IN PAPER AND FAULTS IN TRANSFORMER OIL

Faults in paper are generally considered as more serious than faults in oil, because paper

is often locat5d in areas of high electric field (in the windings, or as voltage barriers), and

the destruction of paper insulation may lead to short circuits or severe arcing. Faults in

paper, fortunately, are much less frequent than faults in oil (typically, in 10 % of cases

only), however, because of the more serious consequences, their detection by DGA or

other means is of great interest.

A popular way of detecting faults in paper by DGA is by looking at the CO2 to CO ratio.

Values < 3 are a good indication of faults in paper of a temperature > 200 to 300°C

(including arcing), where paper degrades very rapidly or even carbonizes. However, there

is always a large background of CO and CO2 in oil (except in the first years of operation

of the transformers) so that caution should be exercised when interpreting the value of

this ratio. Using increment values of CO and CO2 over the last analysis are preferable, but

the uncertainty on the incremented ratio is high and should be calculated to determine its

reliability.

Values of the CO2 to CO ratio > 10 are also an indication of thermal faults in paper at

temperatures < 150 °C, but such temperatures have only a long term aging effect on paper

and on the reduction of transformer life, which can be more precisely evaluated by furans

formation, (when regular kraft, not thermally-upgraded paper, is used).

A sharp increase in the formation of furans may in some cases be a confirmation of faults

in paper at temperatures > 250 °C.

4.9 SAMPLING OF LIQUID INSULATION AND GAS EXTRACTION FROM

OIL

DGA has become very renowned as an effective method that aims at predicting the life

span of transformer. The quality and accuracy of DGA depends on right performance of

the method. The first part consist of appropriate oil sampling, the second part consist of

extraction of dissolved gases and third part for qualitative and quantitative analysis of

extracted gas by gas chromatography or spectroscopy. This portion describes oil sampling

procedure to be adopted for DGA analysis and the precautions to be taken during oil

sampling. Influence of light, air bubbles and temperature on storage of oil sample is

examined.

4.9.1 Sampling Procedures

The validity of the test results is dependent upon being certain that the oil sample is truly

representative of the oil in the equipment. Thus, it is essential that the sample container of

65

whatever type be free of contaminants, that the point of sampling within the equipment

will produce a representative sample, and that sampling taps and connecting tubing be

purged before the sample is taken. Ordinarily, samples for physical tests can be taken in

1-L (1-ft) glass bottles, plastic containers with screw top, Teflon-lined containers, or

stainless steel cylinders. Glass bottles may either be cork or glass stopper or fitted with

screw caps having cork or aluminum liners (inserts). Corks should be of good quality. DO

NOT use rubber stoppers. Gas-in-oil samples may be taken in 150 or 1,000-mL stainless

steel cylinders the 1,000-mL cylinders provide enough samples for both physical and gas-

in-oil tests. Steel containers, except Teflon lined, should be rinsed in chlorothene or

another comparable nonflammable solvent, dried, and washed in strong soapsuds. Then

they should be thoroughly rinsed with water, dried in an oven at 105°C (220°F) for

several hours, and removed from the oven. They should be corked while still warm. As

the bottles cool, dipping the necks in wax should seal them, then; the bottles should be

stored for future use.

These bottles should be opened only when the bottle temperature and the ambient

temperature are the same or nearly so. Because of the importance of properly cleaned and

treated containers, laboratory-approved containers should be used. Oil samples in glass or

other clear containers should be kept out of sunlight. Sunlight will break down materials

in the oil and affect the test results.

4.9.2 Taking Gas Samples for Gas Chromatograph Analysis

This procedure has been developed to maintain uniformity of all gas samples taken in the

field while maintaining the principal objective of obtaining a representative sample that is

free of all contaminants. Special stainless steel or glass cylinders are to be used for

collecting samples of gas for use with the gas chromatograph. These containers are NOT

to be used for any other purpose.

If a laboratory performs the required testing, they normally clean the container to

eliminate all contaminants. Special care should be taken to avoid getting moisture in the

sample since moisture can damage the analyzing equipment.

4.10 INFLUENCE OF STORAGE OF OIL SAMPLES ON DGA

Oil sample containers should be free from contamination and air tight sealed. Sample

container should be fully filled up and kept without air. Adequate sample storage is of

vital importance to obtain accurate results. Some experiments were conducted in order to

investigate the influence of factors such as air bubbles, light and temperature on stored oil

samples [113, 114]. Samples were stored for certain periods and gas in oil analysis was

66

conducted .The oil sample is introduced into a container and is steadily agitated and re-

circulated at constant temperature in order to establish the equilibrium between headspace

gases and oil. Attention was focused on the concentration of hydrogen as a critical

component.

4.10.1 Effect of Air Bubbles

Air bubbles of different volumes were introduced into the sample and then stored in

darkness for 4 days. Other samples without air bubbles were also stored in darkness as a

reference for comparison. It was observed that concentration of H2, CO, CH4 tends to

decrease as the volume of air bubble increases see Fig. 4.3. Especially the concentration

of H2 was significantly affected by a decrease of approx. 35% when air volume is more

than 1% of the sample volume. Bigger air bubbles allow larger diffusion of the less

soluble components in oil; hence there will be lower concentration of dissolved gases in

oil resulting in erroneous DGA result.

Figure 4.3: Influence of air bubbles of different volume on DGA results [113, 114]

4.10.2 Effect of Temperature

Generally samples should be stored at ambient temperature and in darkness. However

during sample transportation to laboratories, it can be exposed to high temperatures. The

test was carried at 90º C in darkness without air, Concentration remained almost

invariable and therefore storage at relatively high temperature did not cause significant

variation see Fig.4.4.

Sometimes Stray gassing is observed i.e. increased production of hydrogen and in some

cases also ethane and methane because of oil interactions with metallic parts such as

copper when they are heated at relatively low temperatures. Stray gassing can be

mistaken for a fictitious fault and DGA analysis can lead to wrong interpretation.

67

Figure 4.4: Stray gassing effect at 90 0C [113, 114]

4.10.3 Effect of Light

The tests were done with samples prepared with air-saturated oil of new and aged

condition. Result shows that concentration of H2 and CO2 were most affected. In case of

new oil, the concentration of CO reduces 12% when stored in darkness, while H2

concentration does not suffer any change since its concentration is less than 5 ppm.

It was seen that under the influence of sunlight the concentration of H2 increases up to

24% while there is no change in CO2 see Fig. 4.5 and Fig. 4.6. In case of darkness H2

concentration increases approx. 10% due to further reactions occurring during storage. No

significant deviation was seen in other components with respect to reference values.

Figure 4.5: Influence of sunlight and darkness on concentration of H2 and CO2 on New

oil[113, 114]

68

Figure 4.6: Influence of sunlight and darkness on concentration of H2 and CO2 on Aged

oil[113, 114]

.4.11 TAKING OIL SAMPLES FOR DGA

For in-service liquid filled electric apparatus, sampling of the liquid dielectric provides a

method to determine the condition of the solid and liquid insulation as well as the

operating condition of the apparatus without opening or de-energizing the apparatus.

Sampling provides a means to check the condition of oil in storage whether it be new or

used, to determine if it complies with specifications. Sampling can also help to determine:

i. If accidental mixing of different dielectric liquids has taken place;

ii. if the method of transportation contaminated the dielectric liquid; and

iii. if the handling equipment to transfer the dielectric liquid contaminated the product.

4.12 TECHNIQUES FOR SAMPLING INSULATING LIQUIDS

There are a number of practices that clearly define the proper way to retrieve samples

from electrical apparatus or storage containers. These practices have been developed over

many years and have incorporated the expertise of many individuals

i. Doble Reference Book on Insulating Liquids and Gases

ii. ASTM D 923: Standard Practice for Sampling Electrical Insulating Liquids

iii. ASTM D 3613: Standard Practice for Sampling Electrical Insulating Oils for Gas

Analysis and Determination of Water Content

iv. IEC 60475: Method of Sampling Liquid Dielectrics

v. IEC 60567: Guide for the Sampling of Gases and of Oil from Oil-filled Electrical

Equipment and for the Analysis of Free and Dissolved Gases.

Sampling procedures and lab handling are usually areas that cause the most problems in

getting an accurate DGA. There are times when atmospheric gases, moisture, or hydrogen

take a sudden leap from one DGA to the next. As has been mentioned, at these times, one

should immediately take another sample to confirm DGA values. It is, of course, possible

69

that the transformer has developed an atmospheric leak, or that a fault has suddenly

occurred inside. More often, the sample has not been taken properly, or it has been

contaminated with atmospheric gases or mishandled in other ways. The sample must be

protected from all contamination, including atmospheric exposure.

Do not take samples from the small sample ports located on the side of the large sample

(drain) valves. These ports are too small to adequately flush the large valve and pipe

nipple connected to the tank; in addition, air can be drawn past the threads and

contaminate the sample. Fluid in the valve and pipe nipple remains dormant during

operation and can be contaminated with moisture, microscopic stem packing particles,

and other particles. The volume of oil in this location can also be contaminated with

gases, especially hydrogen. Hydrogen is one of the easiest gasses to form. With hot sun

on the side of the transformer tank where the sample valve is located, high ambient

temperature, high oil temperature, and captured oil in the sample valve and extension,

hydrogen formed will stay in this area until a sample is drawn.

The large sample (drain) valve can also be contaminated with hydrogen by galvanic

action of dissimilar metals. Sampling valves are usually brass and a brass pipe plug

should be installed when the valve is not being used. If a galvanized or black iron pipe is

installed in a brass valve, the dissimilar metals produce a thermocouple effect, and

circulating currents are produced. As a result, hydrogen is generated in the void between

the plug and valve gate. If the valve is not flushed very thoroughly the DGA will show

high hydrogen.

Oil should not be sampled for DGA purposes when the transformer is at or below

freezing temperature. Test values which are affected by water (such as dielectric strength,

power factor, and dissolved moisture content) will be inaccurate.

This is typically not a problem with conservator transformers. If the transformer is

nitrogen blanketed, look at the pressure/vacuum gauge. If the pressure is positive, go

ahead and take the sample. If the pressure is negative, a vacuum exists at the top of the

transformer. If there is a vacuum at the bottom, air will be pulled in when the sampling

valve is opened. Wait until the pressure gage reads positive before sampling. Pulling in a

volume of air could be disastrous if the transformer is energized.

If negative pressure (vacuum) is not too high, the weight of oil (head) will make positive

pressure at the sample valve, and it will be safe to take a sample. Oil head is about 2.9

feet (2 feet 10.8 inches) of oil per pounds per square inch (psi).

70

If it is important to take the sample even with a vacuum showing at the top, proceed as

described in standards.

Use the sample tubing and adaptors described below to adapt the large sample valve to

1/8 inch tygon tubing. Fill a length (2 to 3 feet) of tygon tubing with new transformer oil

(no air bubbles) and attach one end to the pipe plug and the other end to the small valve.

Open the large sample (drain) valve a small amount and very slowly crack open the small

valve. If oil in the tygon tubing moves toward the transformer, shut off the valves

immediately. Do not allow air to be pulled into the transformer. If oil moves toward the

transformer, there is a vacuum at the sample valve. Wait until the pressure is positive

before taking the DGA sample. If oil is pushed out of the tygon tubing into the waste

container, there is a positive pressure and it is safe to proceed with DGA sampling. Shut

off the valves and configure the tubing and valves to take the sample per the instructions

in the standards.

4.13 GAS EXTRACTION FROM SAMPLE

The key gases are extracted by a degassing technique [110], for which often a so called

Toepler-pump-system is used, Fig. 4.7. The extraction is efficient and gives good results

for a subsequent analysis. This vacuum pump contains usually high amounts of mercury,

thus being a risk for the operating personnel and the environment.

Figure 4.7: Toepler pump for laboratory use [110]

71

Alternative procedures like the so called headspace extraction, where the amount of

dissolved gases is identified from a dissolution of the gases in the liquid into a defined gas

in the headspace of a phinole lead to somewhat different results and is as explained in the

standard IEC 60567 more imprecise.

In this contribution therefore another system for DGA usable even at the transformer-

operating site is introduced. This system consists of a mercury free gas extraction device

combined with a portable gas chromatograph (GC), which identifies the type and amount

of gases after the extraction from the oil sample.

The GC device contains a capillary column, in which the different gases are separated in a

carrier gas flow, for which in most cases Argon (Ar) is used. After passing the ripping-up

column the gases are injected in a Thermal Conductivity Detector (TCD) or a Flame

Ionization Detector (FID), each one sensitive for a special fraction of the detectable gases.

4.14 NEW DGA SYSTEM

With the new DGA system the analysis is done in 2 steps with high selectivity and

accuracy fully compliable with laboratory procedure.

a) Degassing of the taken oil sample through vacuum extraction (according to IEC

60567).

b) Dissolved gas analysis by gas chromatography with TCD (thermal conductivity

detector), FID (Flame Ionization detector) and Methanizer.

Both steps are synchronized by a micro-controller, which guarantees a fast and reliable

measurement routine.

4.15 VACUUM DEGASSING

A new invented vacuum degassing method (Patent DE 102 52 652 4) degases the oil

sample in multiple periods. After the extraction by a 4 step vacuum pump the gases are

enclosed in a vacuum chamber where they are separated. The gases are evacuated from

the vacuum chamber with a vacuum pump; therefore a very high sensitivity can be

reached. The extraction principle is schematically shown in Fig. 4.8 and a photo of the

device is given in Fig. 4.9. Inlet and outlet of the oil sample as well as the switching valve

are microprocessor controlled and a linear pressure sensor measures online the total gas

content (TGC), which is an important parameter concerning bubbling problems.

The injection of the gas samples into the gas chromatograph is made via a fixed mounted

and heated transfer pipe but also an additional port for a withdrawal of gas samples is

installed.

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Figure 4.8: Schematic view of the gas extraction unit

Figure 4.9: Photo of the gas extraction unit [110]

4.16 MOBILE GAS CHROMATOGRAPHY

The analysis of the dissolved gases is done after the automatic or manual injection in an

automatic sample loop (6 port valve) of the gas chromatograph. The gas sample is

separated in an inert gas stream into 11components. This guarantees a high selectivity

with no Interference with other gas signals.

A capillary column (type Carboxen 1010, length 30m x ID 0.32 mm) is used for the

separation of the different compounds, whereas the detection is carried out with a TCD

(Thermal Conductivity Detector) and FID (Flame Ionization Detector). The compounds

CO and CO2 are converted with a methanizer for detection in the FID.

73

This procedure guarantees a very high sensitivity and the determination of all mentioned

key gases including Propane and Propene.

Due to the small size of the GC, which is shown in Fig. 4.10, the device is easy portable

and therefore also suitable for analyses on-site e.g. from free gas in the Buchholz-relay.

This is often very important in cases in which the Buchholz-relay responds after a failure

thus the transformer is protected against expanded failures.

In these cases the free gas accumulated in the relay can be analyzed on the spot, thus a

first statement on the severity of the fault becomes possible. Therefore a two injection

port is installed, thus it is also possible to monitor the Buchholz gases automatically. As

soon as the relay releases, the measurement routine is interrupted and the relay gases are

analyzed accordingly.

Figure 4.10: Mobile GC [110]

4.17 ONLINE DGA SYSTEM

The introduced systems can be used as an online DGA system as shown in Fig. 4.11

which is then controlled by a microprocessor. A serial interface (RS 232) allows

transferring the data to a remote computer. The intervals of the analysis cycle can be

freely defined by the operator (number of analyses per day or hour).

The system has 8 additional analog inputs (0-1 V) for the measurement of other

transformer parameters, like oil temperature, winding temperature, ambient conditions

etc. The connection of these external sensors is realized via relay contacts, which can be

specified by the customer as NC (normally closed) or NO (normally opened).

74

Figure 4.11: Online DGA system [110]

Beside the display of the results in ppm all other operating conditions are registered

online and stored in a database together with all other parameters. The graphical display

of the results is in real time on the monitor, in a table or/and as a test report in a print file.

The data can also be transmitted to a remote computer or a control center. All relevant

data are stored in a measurement file. The results are stored with other measurement

parameters in the history (log file). The history starts with the day of the commissioning.

The trend is shown in a diagram (hour / day / week / month / year) and the operator can

adjust the interval. The integrated expert system will analyze even smallest deviations in

the gas parameters. A plausibility test ensures the accuracy and reproducibility of the test

results.

Furthermore different alarm settings are possible, thus the following incidents lead to an

alarm;

i. Monitoring of all gas parameters (11 gases), excess of one parameter will activate an

alarm and data will be send to a remote control system,

ii. Trend analysis for predictive maintenance for each gas compound if the rise exceeds

beyond predefined threshold levels (considering the temperature of the transformer),

iii. Interruption of power supply,

iv. Status report of the system with automatic self diagnostic function, and

v. Export of all data and alert to a remote control system.

All threshold values can be individually selected for each gas component. The trend

analysis starts with the day of installation and is stored as an unbroken chain of evidence.

The trend will show any irregularity above 0.5 ppm or for organic components 0.1 ppm

on the FID channel. The degree of degradation of the insulation of the transformer can be

75

displayed with a comparison of the different parameters versus the time, which is an

important criterion for prophylactic control of possible defects in the transformer.

A system for DGA was introduced containing a new degassing technique and a mobile

GC, thus these devices can be combined to an online DGA transformer monitoring

system, which could enhance the reliability of transformers significantly.

Uncertainties related to oil sampling can be avoided, when this system is installed online,

thus the condition and gassing history of the transformer could be continuously

monitored.

Comparison tests with a Toepler-pump system and a high precision laboratory GC have

shown a high preciseness and good reproducibility of the system.

4.18 GASES ANALYZED BY DGA

The main gases formed by decomposition of oil and paper are summarized in Table 4.4

below. These gases dissolve in oil or accumulate above it and are analyzed by DGA.

DGA is the technique for detecting and monitoring faults in electrical equipment. The

combustible Concentration Limits differ from country to country, continent to continent

and transformer to transformer. It is not practical to set concentration limits because of

the many variations involved. The Gas Concentrations in the oil depend upon:

a) The volume of oil involved (dilution factors)

b) The age of the transformer (new or old)

c) The type of transformer;

Generator or Transmission

Sealed or free breathing

Construction of Tap changer

Table 4.4: Dissolved gases in transformer oil

Sl. No. Name Symbol

1 Nitrogen N2

2 Oxygen O2

2 Hydrogen H2

4 Carbon Monoxide CO

5 Carbon Dioxide CO2

6 Methane CH4

76

7 Ethane C2H6

8 Ethylene C2H4

9 Acetylene C2H2

Some laboratories also report the contents of C3 and C4 hydrocarbon gases formed.

4.19 FAULTS DETECTION BY DGA

Seven basic types of faults detectable by DGA have been defined:

4.19.1 Partial Discharges (PD) of the corona-type (PD)

Partial discharge is localized dielectric break down of a small portion of liquid or solid

electrical insulation system under high voltage stress, which does not bridge the space

between two conductors. Whereas corona is usually revealed by a relatively steady glow

of brush discharge in air, PD in solid systems is not visible.

Typical examples: discharges in gas bubbles or voids trapped in paper, as a result of poor

drying or poor oil-impregnation.

4.19.2 Discharges of Low Energy (D1)

Paper insulation in the transformers get carbonized under high electric stresses. This is the

way of conducting discharge through the oil between two conducting parts. Therefore,

carbonized paper is trailed under overloaded conditions, as in the process of modifying

the carbon particles in oil form.

Typical examples: Partial discharges of the sparking-type, including carbonized punctures

in paper. Low energy arcing, including surface tracking of paper and carbon particles in

oil.

4.19.3 Discharges of High Energy (D2)

Powerful discharges take place and the paper the damage by excessive carbon recovery.

Very large discharges caused the metal part to melt, thus excessive carbon recovery is

found in the transformer oil. In this fault, flow through the defective regions disables the

transformers.

Typical examples: High energy arcing, flashovers and short circuits with power follow-

through, resulting in extensive damage to paper, large formation of carbon particles in oil,

metal fusion, tripping of the equipment or gas alarms.

4.19.4 Thermal Faults of temperatures < 300°C (T1)

The continuous transformer overloaded conditions in case of emergency becomes the

reason for thermal faults. Under the temperature range less than 3000C, the insulation

paper is brown. Faults T1 are evidenced by paper turning brownish.

77

i. Brown (> 200°C)

ii. Black or Carbonized ( > 300°C)

Typical examples: Overloading, Blocked oil ducts.

4.19.5 Thermal Faults of temperatures between 300 °C and 700°C (T2)

For T2 faults, the temperature range is 300-700 0C. T2 fault is the cause of circulating

currents between the metallic parts in the transformer. Faults T2 are evidenced by:

i. Carbonization of paper

ii. Formation of carbon particles in oil.

Typical examples: Defective contacts, Defective welds, Circulating currents.

4.19.6 Thermal Faults of temperatures > 700 °C (T3)

T3 fault in the transformer happens above 700 0C. This fault is the cause of large

circulating currents in tank and core. Faults T3 are evidenced by:

i. Extensive formation of carbon particles in oil.

ii. Metal Coloration (800°C) or metal fusion (> 1000°C)

Typical examples: Large circulating currents in tank and core, Short circuits in

laminations.

4.20 STANDARDS AND CODES FOR DGA INTERPRETATIONS ADOPTED IN

INDIA

The following international and national standard/ interpretation guides of dissolved gas

analysis for fault diagnosis of power transformers are widely adopted in India and across

the world:

i. IEEE Std. C57-104TM

-2008, “IEEE Guide for the Interpretation of Gases Generated

in Oil- Immersed Transformers,” September 2008.

ii. IEC Publication 60599, “Mineral Oil Impregnated Equipments in Service- Guide to

the Interpretation, of Dissolved and Free Gases Analysis,” March 1999.

iii. IS 10593: 1992(Reaffirmed 2006) & IS-9434: (1992), ―Indian Standard Method of

Evaluating the Analysis of Gases in Oil-Filled Electrical Equipment in Service‟‟ (First

Revision), 1995.

iv. ASTM D 3612- Determination of Individual Dissolved Gases ―Laboratory methods for

analyzing the gas blanket and the gases extracted from oil”.

v. ANSI- American National Standards Institute. www.ansi.org/

vi. FIST (Facilities Instructions Standards and Techniques Vol.-3-31), ―Transformer

Diagnostic‖, US Department of the Interior Bureau of Reclamation, June 2003.

78

vii. CIGRE Working Group 12.18-2002, ―Life Management of Transformers-Guidelines

for Life Management Techniques of Power Transformers,‖ 2002.

4.21 OTHER TRANSFORMER CHEMISTRY SERVICES DURING

INTERPRETATION

Various other evaluations and compensations are given due considerations for proper

fault interpretations from the history of transformer. Some of those are as:

i. Key gases: CSUS values (Age compensated)

ii. BS 5800/IEC 599 ratios (providing the Total Combustible Gases present are above

300 ppm)

iii. Rogers Ratio‘s

iv. Trend (Production rates of gases) Morgan-Schaffer Tables

v. Total Combustible Gas Production Rates TDCG

vi. Total Combustible Gas Westinghouse Guidelines

vii. Age of transformer.

viii. History of transformer (Repaired, degasses, etc).

Analysing insulating oil taken from transformers is a unique way of identifying problems

occurring within a transformer.

By identifying and quantifying the gases found in transformer oil, the condition of the

transformer can be monitored.

If faults are found to be occurring, outages can be planned and the fault can be rectified

before major damage can occur.

The interpretation of transformer oil gas analysis is still an art and not an exact science.

The interpretation should be left to a specialist and his advice and recommendations

should be followed. Samples should be taken regularly and records are kept by utilities

with transformers. Some of the typical values of gas interpretations are shown in below

Tables 4.5 to Table 4.10 around the world [111].

Table 4.5 issued by California State University of US for total combustible gases for their

individual normal and abnormal ranges of gases. This table only shows five possible

faults given as below:

Table 4.5: California State University Sacremento guidelines for combustible gas

GAS NORMAL ABNORMAL INTERPRETATION

HYDROGEN(H2) <150 ppm >1000 ppm Arcing corona

CARBON MONOXIDE(CO) <500 ppm >1000 ppm Severe Overloading

79

CARBON DIOXIDE(CO2) <10000 ppm >15000 ppm Severe Overloading

METHANE(CH4) <25 ppm >80 ppm Sparking

ETHANE(C2H6) <10 ppm >35 ppm Local overheating

ETHYLENE(C2H4) <20 ppm >100 ppm Severe Overheating

ECYTYLENE(C2H2) <15 ppm >70 ppm Arcing

NITROGEN (N2) 1-10% NA -

OXYGEN (O2) 0.03% >0.5% Combustibles

Table 4.6 again issued in addition to above table to show the comparison between three

different followed standards and particularly to provide compensation for the age of

transformer to the existing gas levels.

Table 4.6 California State University Sacremento guidelines for combustible gas

GAS Dornenburg/

Stritt.

IEEE Bureau of

Reclamation(US)

Age

Compensated

HYDROGEN(H2) 200 100 500 20n+50

CARBON

MONOXIDE(CO)

500 350-

720

750 25+500-

110n+710

METHANE(CH4) 50 120 125 20n+50

ETHANE(C2H6) 35 65 75 20n+50

ETHYLENE(C2H4) 80 50 175 20n+50

ECYTYLENE(C2H2) 5 35 7 5n+10

TDCG(total above) 6000 2500 10000 100n+1500

n=years in

service

Table 4.7 is guidelines of three standards to show the different gas levels for generator

transformers and transmission transformers for interpretation of faults to present the

violation limits.

Table 4.7: CEGB/ANSI/IEEE Guide for gas concentration limits in ppm V/V

GAS GENERATOR

TRANSFORMER

TRANSMISSION

TRANSFORMER

HYDROGEN(H2) 240 100

80

CARBON

MONOXIDE(CO)

580 350

METHANE(CH4) 160 120

ETHANE(C2H6) 115 65

ETHYLENE(C2H4) 190 30

ECYTELENE(C2H2) 11 35

Table 4.8 shows the limits of the individual gases used in Canada, Switzerland and

Finland as standards for assessing the faults.

Table 4.8: Other International gas concentration limits in ppm V/V

GAS HYDRO

QUEBEC

CANADA

BBC

SWITZERLAND

OY

STROMBERG

FINLAND

HYDROGEN(H2) 250 200 100

CARBON

MONOXIDE(CO)

850 1000 500

METHANE(CH4) 33 50 100

ETHANE(C2H6) 15 15 150

ETHYLENE(C2H4) 40 60 100

ECYTELENE(C2H2) 25 15 30

Table 4.9 provides the individual gas limits for different range of transformer ratings

issued by SERC, Japan regulating authority, which is adopted by most their utilities for

transformer fault assessments.

Table 4.9: SERC – Japan limiting values in ppm V/V

GAS TRANSFORMERS

>275KV & - >10MVA

TRANSFORMERS

>275KV & - <10MVA

TRANSFORMERS

> 500KV

H2 400 400 300

CO 300 300 200

CH4 150 200 100

C2H6 150 150 50

C2H4 200 300 100

TCG 700 1000 400

81

Table 4.10 issued by EDC, France showing separate ranges of individual gas limits for

generator and transmission transformers without on-load tap changers.

Table 4.10: EDF – France Generator/Transmission transformers without OLTCs

GAS GENERATOR

TRANSFORMER

TRANSMISSION

TRANSFORMER

HYDROGEN(H2) 33 130

CARBON MONOXIDE(CO) 770 1000

METHANE(CH4) 44 130

ETHANE(C2H6) 33 150

ETHYLENE(C2H4) 11 44

ECYTELENE(C2H2) 0.4 0.4

4.22 DIFFERENT DGA METHODS

If DGA values are above typical concentration values and/or rates of increase, an actual

fault in the transformer is probable, and diagnostic methods may be used for its

identification.

The main diagnostic methods used are:

i. The IEEE methods (Key gases methods, Rogers‘s and Doernenburg‘s)

ii. The IEC ratio codes

iii. The Duval Triangle

iv. The CIGRE standards

Out of above methods, following are used in thesis.

4.23 KEY GASES METHOD (C-57-104TM

, 2008)

The principle of the Key Gas method [41] is based on the quantity of fault gases released

from the insulating oil when a fault occurs which in turn increase the temperature in the

power transformer.

4.23.1 Evaluation of Possible Fault Type by the Key Gas Method

Previous discussions of the dependence on temperature of the types of oil and cellulose

decomposition gases provides the basis for the qualitative determination of fault types

from the gases that are typical, or predominant, at various temperatures. These significant

gases and proportions are called ―Key Gases‖. Fig 4.12 to Fig.4.15 indicate, these ―Key

Gases‖ and relative proportions for the four general fault types. The presence of the fault

gases depends on the temperature or energy that will break the link or relation of the

82

insulating oil chemical structure. This method uses the individual gas rather than the

calculation of gas ratios for detecting fault.

Figure 4.12: Key gases (Over Heating, Oil)

Figure 4.13: Key gases (Over Heating, Paper)

Figure 4.14: Key gases (Partial Discharge)

Figure 4.15 Key gases (Arcing)

83

4.24 EVALUATION OF TYPES OF POSSIBLE FAULT BY ANALYSIS OF THE

SEPARATE COMBUSTIBLE GASES GENERATED

The use of gas ratios to indicate a single possible fault type is an empirical process based

upon the experience of each individual investigator in correlating the gas analyses of

many units with the fault type subsequently assigned as the cause for disturbance or

failure when the unit was examined. This process was attributed to Dernenburg‘s and

subsequently confirmed by Roger‘s on European systems, from which the bulk of the

diagnostic correlation is obtained. U.S. investigators have applied the European rules to

units on U.S. systems with varying degrees of success; however, a U.S. database of

comparable size to the European reports does not exist.

The diagnostic theories based upon the thermal degradation principles oil and paper

decompositions employ an array of ratios of certain key combustible gases as the fault

type indicators. These five ratios are:

i. Ratio 1 (R1) = CH4/H2

ii. Ratio 2 (R2) = C2H2/C2H4

iii. Ratio 3 (R3) = C2H2/CH4

iv. Ratio 4 (R4) = C2H6/C2H2

v. Ratio 5 (R5) = C2H4/C2H6

The first ratio method (Dornenburg‘s given below) utilizes Ratios 1, 2, 3, and 4. This

procedure requires significant levels of the gases to be present in order for the diagnosis

to be valid.

The second method (Rogers given below) utilizes Ratios 1, 2, and 5. The Roger‘s method

does not depend on specific gas concentrations to exist in the transformer for the

diagnosis to be valid. However, it suggests that the method be used only when the normal

limits of the individual gases have been exceeded.

4.25 THE DORNENBURG’S RATIO METHOD

This method started out as only two ratios:

CH4 /H2 & C2H2/C2H4

The ratios were plotted on a log-log scale. The areas corresponded to thermal

deterioration, arcing and partial discharge. By two ratio method too many faults missed

and then went to four ratios.

Ratio 1 (R1) = CH4/H2

Ratio 2 (R2) = C2H2/C2H4

Ratio 3 (R3) = C2H2/CH4

84

Ratio 4 (R4) = C2H6/C2H2

Used to determine three types of general faults;

i. Thermal faults

ii. Electrical Faults, low intensity discharges

iii. Electrical Faults, high intensity arcing

The Dornenburg‘s ratio method suggests the existence of three general fault types as

discussed. The method utilizes gas concentrations from which Ratios 1, 2, 3, and 4 are

calculated. According to IEEE Standard C-57.104TM

-1991, the step-by-step procedure to

diagnose faults using Dornenburg‘s ratio method as follows:

The values for these gases are first compared to special concentration limits in Table 4.11

(see Steps 2, 3, and 4 in Fig. 4.16)—to ascertain whether there really is a problem with

the unit and then whether there is sufficient generation of each gas for the ratio analysis to

be applicable. Then the ratios in the order Ratio 1, Ratio 2, Ratio 3, and Ratio 4 are

compared to limiting values, providing a suggested fault diagnosis as given in Table 4.12

and. Table 4.11 gives the limiting values for ratios of gases dissolved in the oil and gases

obtained from the transformer gas space or gas relay.

Table 4.11: Concentration limits of dissolved gas

Key Gases Concentrations limits

[µL/L(ppm)]

H2 100

CH4 120

C2H6 65

C2H4 50

C2H2 1

CO 350

The flow chart in Fig. 4.16 illustrates the step-by-step application of the Dornenburg‘s

ratio method for gases extracted from the transformer oil only. Exactly the same

procedure is followed for gases obtained from the gas space or gas relays, except the

limiting values for the ratios will be those appropriate for gas space.

Descriptions of the steps indicated in flow-chart are as follows:

Step 1; Gas concentrations are obtained by extracting the gases and separating them by

chromatograph

85

Step 2; If at least one of the gas concentrations [in microliters/liter, (ppm)] for H2, CH4,

C2H2, and C2H4 exceeds twice the values for concentration limit (see Table 4.11) and one

of the other two gases exceeds the values for limit L1, the unit is considered faulty;

proceed to Step 3 to determine validity of the ratio procedure.

Step 3; Determining validity of ratio procedure: If at least one of the gases in each ratio

R1, R2, R3, or R4 exceeds concentration limits, the ratio procedure is valid; otherwise,

the ratios are not significant, and the unit should be re-sampled and investigated by

alternate procedures.

Step 4; Assuming that the ratio analysis is valid, each successive ratio is compared to the

values obtained from Table 4.12. in the order R1, R2, R3, and R4.

Step 5; If all succeeding ratios for a specific fault type fall within the given limit values,

the suggested diagnosis is valid.

i. Criteria for application - a fault exists

a) One Gas > 2 times the minimum level

b) At least one of two other gases > minimum level

ii. Determine Validity, concentration limits norm test

One gas in each ratio > minimum

iii. Compare ratios to Fault Diagnosis Table

iv. All fall within one condition-valid diagnosis

Table 4.12: Dornenburg‘s ratios for key gases

Suggested Fault

Diagnosis

Ratio1 (R1)

CH4/H2

Ratio2 (R2)

C2H2/C2H4

Ratio3 (R3)

C2H2/CH4

Ratio4 (R4)

C2H6/C2H2

Oil Gas

Space

Oil Gas

Space

Oil Gas

Space

Oil Gas

Space

1.Thermal decomposition >1.0 >0.1 <0.75 <1.0 <0.3 <0.1 >0.4 >0.2

2.Partial discharge (low-

intensity PD)

<0.1 <0.01 Not significant <0.3 <0.1 >0.4 >0.2

3.Arcing (high-intensity

PD)

>0.1- -

<1.0

>0.01--

<0.1

>0.75 >1.0 >0.3 >0.1 <0.4 <0.2

86

Figure 4.16: Dornenburg‘s ratio method flow-chart

4.26 THE MODIFIED ROGER’S RATIO METHOD

Roger‘s originally developed four ratios CH4/H2 (R1), C2H6/CH4, C2H4/C2H6 (R5) and

C2H2/C2H4 (R2). The Roger‘s ratio method follows the same general procedure as the

Dornenburg method, using only three ratios (R1, R2, and R5) are used. This method,

shown in the step-by-step flow chart (see Fig. 4.17), is also based on the thermal

degradation principles. The validity of this method is based on correlation of the results of

a much larger number of failure investigations with the gas analysis for each case. But, as

with the Dornenburg method, the Roger‘s ratios can give ratios that do not fit into the

diagnostic codes. Therefore, other analytical methods by TCDG and Key Gas ratio should

be considered, as well as other options. These ratios, according to Rogers, are applicable

to both gases taken from the gas space (or relay) and gases extracted from the oil. The

fault types (cases) given in Table 4.13 have been chosen by combining some cases from

the number of fault types originally suggested by Roger.

Table 4.13: Roger‘s ratios for key gases

Case Suggested Fault Diagnosis Ratio1 (R1)

CH4/H2

Ratio2 (R2)

C2H2/C2H4

Ratio5 (R5)

C2H2/CH4

0 Unit Normal >0.1 to <1.0 <0.1 <1.0

1 Low-energy density arcing—PD@

<0.1 <0.1 <1.0

2 Arcing--high-energy discharge 0.1 to 1.0 0.1 to 3.0 >3.0

3 Low temperature thermal >0.1 to <1.0 <0.1 0.1 to 3.0

4 Thermal <7000C >0.1 <0.1 0.1 to 3.0

5 Thermal >7000C >0.1 <0.1 >3.0

@There will be a tendency for the ratios R2 and R5 to increase to a ratio above 3.0 as the

discharge develops in intensity.

87

Figure 4.17: Modified Roger‘s ratio method Flow-chart

4.27 DUVAL TRIANGLE METHOD

The Duval Triangle was first developed in 1974. It uses three hydrocarbon gases only

(CH4, C2H4 and C2H2). These three gases correspond to the increasing levels of energy

necessary to generate gases in transformers in service. The Triangle method is indicated

in Fig. 4.18.

Figure 4.18: Duval Triangle 1

88

Figure 4.19: Duval triangle indicating regions of different faults

In addition to the six zones of individual faults as mentioned (PD, D1, D2, T1, T2 or T3),

an intermediate zone DT has been attributed to mixtures of electrical and thermal faults in

the transformer. Colour codes for Duval Triangle shown in Fig. 4.19 are used for an easy

interpretation. C2H2 and C2H4 are used in all other interpretation methods to represent

high energy faults (such as arcs) and high temperature faults. H2 is preferred in several of

these methods to represent very low energy faults such as PDs, where it is produced in

large quantities. CH4, however, is also representative of such faults and always formed in

addition to H2 in these faults, in smaller but still large enough amounts to be quantified.

CH4 has been chosen for the Duval triangle because it not only allows to identify these

faults, but provides better overall diagnosis results for all the other types of faults than

when using H2.

The following are the some more methods used by some of the utilities in India to cross

verify the faults in transformers and many other countries subjected to their weather and

tropical conditions. A few are discussed as:

4.28 IEC 60599

This method identifies six different fault types listed below. This interpretation method

adopted in most of the part of the world. Almost IEEE and IS standards followed in India

are extracted from these interpretation methods.

a) PD: Partial Discharge

b) D1: Discharge of low energy

c) D2: Discharge of high energy

d) T1: Thermal fault, t <300°C

e) T2: Thermal fault, 300°C < t < 700 °C

f) T3: Thermal fault, t > 700 °C

89

This method uses a combination of ratios (based on Roger‘s Ratios), gas concentrations

and rates of gas increase between the consecutive sample testings.

4.28.1 IEC 60599 Ratio-Fault Diagnosis

Fault diagnosis becomes the combined outcome of gas ratios R1, R2 and R5 along with

the limits of the ratios to represent one of the fault, out of the six faults. This is shown in

Table 4.14.

Table 4.14: IEC 60599 ratio-fault diagnosis

R2

C2H2/C2H4

R1

CH4/H2

R5

C2H4/C2H6

Faults

NS <0.1 <0.2 PD

>1 0.1-0.5 >1 D1-Low Energy

0.6-2.5 0.1-1 >2 D2-High Energy

NS >1 (NS) <1 T1- <3000C

<0.1 >1 1-4 T2- >3000C - <700

0C

<0.2 >1 >4 Thermal (T3)>7000C

NS- Not Significant regardless of values.

4.29.2 Rates of Gas Increase

The second procedure to divide the faults depends upon the percentage of gas increase

between the recommended periodicity during the fault monitoring of the transformer.

i. If gas increases >10% per month above typical levels = active fault.

ii. If gas increases >50% per week or evolving faults of higher energy = serious faults

4.29.3 IEC 60599 Typical Gas Levels

The Table 4.15 is given by IEC- 60599 standard to follow strictly the safe gas levels for

evaluation of faults in different types of the transformers.

Table 4.15: IEC 60599 typical gas levels

H2 CO CH4 C2H6 C2H4 C2H2 CO2

IEC 60599

Typical Range

60-150 540-900 40-110 50-90 60-280 3-50 5100-

13000

Communicating

OLTC

75-150 400-850 35-130 50-70 110-250 80-270 5300-

12000

Note in IEC 60599: Typical values are higher in sealed transformers than free breathing

transformers.

90

4.30 NOMOGRAPH METHOD

The logarithmic nomograph method was developed by J. O. Church. This method

combines the fault gas ratio concept with the Key Gas threshold value in order to improve

the accuracy of fault diagnosis. It was intended to provide both a graphic presentation of

fault-gas data and the means to interpret its significance. The nomograph consists of a

series of vertical logarithmic scales representing the concentrations of the individual

gases as shown in Fig. 4.20.

With this method, straight lines are drawn between adjacent scales to connect the points

representing the values of the individual gas concentration. The slopes of these lines are

the diagnostic criteria for determining the type of fault. The key at the bottom of the chart

between the two axes indicates the fault type for the two axes. A visual comparison of the

slopes of the line segments with the keys given at the bottom of the Nomograph is all

needed to identify the type of fault. The position of the lines relative to the concentration

scales provides a means of assessing the severity of the fault.

Figure 4.20: Logarithmic nomograph [112]

Each vertical scale has a threshold value labeled with an arrow. For the slope of a line to

be considered significant, at least one of the two tie-points should lie above a threshold

value. If neither tie-point lies above a threshold value then the fault indication of that

slope is not considered significant.

4.31 DENKYOKEN METHOD

A special committee on ―Conservation and Council of oil-insulated components by

diagnosis of gas in oil‖ in Japan developed the Denkyyon Method. This is the diagnostic

method recommended by Electrical Cooperative Research Association (ECRA). This

method is based on the amount of H2, CH4, C2H6, C2H4 and C2H2 gases in ppm, the most

abundant gas is taken as 100% and the all other gases are then impressed with reference

91

to the value for a given specimen data. Thirteen different patents are designated for cause

of failure in oil-insulated power apparatus.

4.32 CIGRE’S METHOD

The method is use the key gas ratios as well as gas concentration for faults diagnosis. The

five key gas ratios those are considered are C2H2/C2H6, H2/CH4, C2H4/C2H6, C2H2/H2, and

CO/CO2. The key gas concentrations are C2H2, H2, sum of carbon hydrides, CO and CO2.

The ratio ranges and concentration limits are suggested. There is no combined application

of both these methods in case all the ratios and concentration are below the limits, a

transformer is considered to be healthy. The advantage of this method is that two or more

faults can be detected.

4.33 NBR7274 METHOD

This Brazilian standard is based on IEC 599/78 and the five gases considered H2, CH4,

C2H6, C2H4 and C2H2 in ppm. It employs the three ratios C2H2/C2H6, CH4/H2 and

C2H4/C2H6 based on the three ratios. The faults are classified in eight types including

normal ageing.

4.34 IS 10593:2006 Method

The Indian Standard is identical to the IEC 60599/1999 and was adopted by Bureau of

Indian Standards. On the recommendations of Fluids for Electrochemical Applications

Sectional Committee and approved by the Electrochemical Division Council. In this

adopted standard reference appears to certain International Standards for which can be

summarized into six types:

i. The methods have been developed based on experience and judgments of the human

experts only.

ii. Due to the in-completeness of the possible ratio combination and uncertainty of the

validity of the defined ranges of key gas ratios, there is high degree of inconsistency

and ambiguity.

iii. When these schemes are applied some times the decision is dependent only on the

expert.

iv. Concurrently within the power transformer multiple faults occur, all these methods

are unable to detect new or unknown faults.

v. The ratio methods do not cover all ranges of data.

92

4.35 COMPARATIVE ANALYSIS OF DIFFERENT DGA METHODS (KEY GAS,

DORNENBURG’S, ROGER’S RATIO & DUVAL TRIANGLE METHOD)

To understand the accuracy and simplicity among the different fault interpretation

methods, two case studies have been presented and shown in Appendix-A. It shows the

advantages of Duval triangle method over other convention methods of fault

interpretations. Table 4.16 reveals that Duval triangle method provides least unresolved

diagnosis and diagnosis of power transformers..

Table 4.16: Relative Performance of Different Methods

4.36 SUMMARY

This chapter concludes with the mechanism of decomposition of oil and the formation of

many gases in the transformer oil at different temperature ranges. It also shows how the

solubility of the gases changes with temperature in the oil? Various sampling

standards/procedures are listed. Safe and error free sampling procedures and gas

extraction process for the transformer and the oil samples are also explained. Small

descriptions of the systems and apparatus for DGA are included. Some of the new

degassing techniques and mobile GC for on-line monitoring are introduced. These

devices can be combined to an off-line/online DGA fault diagnosis/monitoring systems,

which can enhance the reliability of transformers significantly.

A list of gases produced in the oil during degradation is given in the beginning of the

chapter. The faults detectable by DGA in the transformers as per IEEE/IEC standards are

briefly described. Since gases limit interpretation is counted to be an art, the gases

limiting tables suggested in different standards, countries and organizations subjected to

their local environmental conditions. IEEE/IEC standards adopted in India are largely

explained in this chapter. Brief introduction of some others are also given. From the case

study conducted on fault analysis on some given transformer reports, the study concludes

that Duval triangle method analysis have low uncertainty regarding fault interpretation

compare to other conventional methods.

DGA Method % Unresolved diagnosis % Wrong diagnosis % Total

Key gases 0 58 58

Roger‘s 33 5 38

Doernenburg‘s 26 3 29

IEC 15 8 23

Duval triangle 0 4 4

93

CHAPTER 5

FAULT DIAGNOSIS WITH DUVAL TRIANGLE

5.1 INTRODUCTION

In this chapter Interpretation of DGA in Power Transformers is presented, with special

emphasis and use on the Duval Triangle method. This method uses three hydrocarbon

gases only (CH4, C

2H

4 and C

2H

2). These three gases correspond to the increasing levels of

energy necessary to generate gases in transformers in service. In addition to the six zones

of individual faults an intermediate zone DT has been attributed to mixtures of electrical

and thermal faults in the transformer.

5.2 FAULT DIAGNOSIS BY DUVAL TRIANGLE METHOD

The main gases formed as a result of electrical and thermal faults in transformers and

evaluated by DGA are H2, CH4, C2H2, C2H4, C2H2, CO, and CO2, whose relative

concentrations depend on the fault type. A general rule, based on thermodynamic

considerations, is that the degree of chemical un-saturation of the gases formed is related

to the energy density of the fault. Acetylene (C2H2) is thus mainly associated with arcing,

where temperatures reach several thousand degrees, ethylene (CH4,) with hot spots

between 150 °C and 1000 °C, and hydrogen (H2) with the cold gas plasma of corona

discharges. Although mixtures of all gases, including the other saturated hydrocarbons

(CH4, C2H6), are usually obtained in most cases of faults. Their relative proportions have

been correlated, through empirical observations and laboratory simulations, with the

various types of fault commonly encountered in transformers in service. The most widely

used tool for that purpose is the IEC/IEEE ratio method. One drawback of this method in

its present form is that a significant number of DGA results in service fall outside the

proposed codes, and cannot be diagnosed. Other methods that overcome this limitation

have therefore been developed, shown in Fig. 5.1 based on a triangular representation and

Hydro-Quebec experience using nomographs.

Figure 5.1: Duval triangle

94

a. 1. High-energy arcing (I>20 In)

b. 2. Low- energy arcing. Tracking

c. 3. Corona discharges

d. 4. Hpt spots, T < 200 0C

e. 5. Hot spots, 200 < T < 400 0

C

f. 6. Hot spots, T > 400 0C

To apply Duval triangle, thrre gses are changed to their percentages from the sum of these

gases. These three percentages can be represented on the each side of the equiviletral

triangle to show the graphical presentation for fault analysis. The triangular coordinates

are represented as given below:

% CH4 = for, x = [ CH4] in ppm (5.1)

% C2H4 = for, y = [ C2H4] in ppm (5.2)

% C2H2 = for, z = [ C2H2 ] in ppm (5.3)

5.3 IMPROVED VERSION OF DUVAL TRIANGLE 1 METHOD

Several methods of interpretation of DGA in transformers in service are provided in the

IEEE Guide C-57.104TM

[41], IEC Standard 60599 [45], as well as in published reviews

on the subject. The Duval Triangle method is described in the IEC Standard and in these

published reviews, however, users sometimes are not quite at ease with the use of

triangular coordinates.

The performance of the improved Duval Triangle with CH4

might be related to the fact

that H2

diffuses much more rapidly than the hydrocarbon gases from the oil through

gaskets and even metal welds. Therefore, gas ratios using H2

are probably more affected

by the loss of this gas than those using hydrocarbons gases only, which have much lower

and comparable diffusion rates.

The three sides of the Triangle are expressed in triangular coordinates (X, Y, Z)

representing the relative proportions of CH4,

C2H

4 and C

2H

2, from 0% to 100% for each

gas.

In order to display a DGA result in the Duval Triangle 1 method, the following procedure

must be adopted. One must start with the concentrations of the three gases, (CH4) = A,

(C2H

4) = B and (C

2H

2) = C, in ppm.

i. Calculate the sum of these three values: (CH4

+ C2H

4 + C

2H

2) = S, in ppm.

95

ii. Calculate the relative proportion of the three gases, in %:

X = % CH4

= 100 (A/S), Y = % C2H

4 = 100 (B/S), Z = % C

2H

2 = 100 (C/S).

X, Y and Z are necessarily between 0 and 100%, and (X+Y+Z) should always = 100 %.

iii. Plotting X, Y and Z in the Triangle, provides only one point in the Duval Triangle.

For example, if the DGA results are A = B = C = 100 ppm, X = Y = Z = 33.3%, which

corresponds to only one point in the centre of the Triangle, as indicated in Figure 5.2. The

zone in which the (X, Y, Z) point falls in the Duval Triangle allows to identify the fault

responsible for the DGA results. Plotting the (X, Y, Z) point in the Triangle can also be

done manually, preferably using a triangular graphical paper such as in Fig. 5.2 for better

precision.

Figure 5.2: Duval triangle 1 graphical plot

Zone boundaries in the Triangle [65-68] have been deduced empirically from a large

number of cases of faults visually inspected in transformers worldwide over the last 60

years by CIGRE and other resources in Fig. 5.3. Well documented and reliable new cases

of faults inspected in service may be used to confirm or re-adjust slightly these

boundaries.

96

Figure 5.3: Actual cases of faults visually inspected in different transformers

5.4 THE INFLUENCE OF LABORATORY ACCURACY ON FAULT DIAGNOSIS

The accuracy of DGA diagnosis, whatever the diagnosis method used, depends greatly on

the accuracy and reliability of the DGA results coming from the laboratory. Note that, by

convention among chemists, accuracy is represented by the difference with actual value

(the analytical error in %), so that higher (better) accuracies are represented by a smaller

number in %.

A few laboratories worldwide provide very accurate results, with an accuracy higher (or

error lower) than ±5% at routine gas concentration levels (typically, above 10 ppm for

hydrocarbon gases). Some others are known to provide very inaccurate results (±50%).

In-between, the average accuracy of laboratories worldwide has been evaluated by

CIGRE TF11 as ~ ±15% at routine levels. The average accuracy worsens rapidly to ~

35% at lower concentration levels (between 2 and 10 ppm for hydrocarbon gases), and

even more so (to 100% and more) as concentrations approach analytical detection limits.

This is illustrated in Fig. 5.4, where the diagnosis uncertainty corresponding to the

various DGA cases.

97

Figure 5.4: Uncertainty on diagnoses for many cases

The coloured polygons from the Fig. 5.4 represents the 10% uncertainty around the fault

in the each zone boundaries. This range of uncertainty from the laboratory results are

given in Table 5.1.

Table 5.1: Uncertainty on laboratory results

Sl. No. Fault CH4 C2H4 C2H2

1 PD 99 1 0

9.9 0.1 0

2 D1 38 12 50

3.8 1.2 5

3 D2 15 50 35

1.5 5 3.5

4 T2 69 30 1

6.9 3 0.1

5 T3 20 75 5

2 7.5 0.5

The more inaccurate the laboratory results, the larger the uncertainty on the diagnosis, as

illustrated in Fig. 5.5.

Figure 5.5: Diagnosis uncertainties to laboratory analytical accuracy of +- 15,30,50&75%

When a polygon crosses two or more zones, a wrong or uncertain diagnosis may result.

This may have serious consequences for the equipment if for example an arcing problem

is mistakenly diagnosed as a less severe thermal fault. In order to get good reliable

98

diagnoses, laboratory accuracy should below ±10%. Between ±10% and ±40%, diagnoses

will likely become more and more uncertain, and above ±40% they are totally

meaningless. DGA users are therefore strongly recommended to verify the accuracy of

their laboratories, using samples of gas-in-oil national/international standards.

DGA users should also always look at inconsistencies in the DGA results, for instance

values going up and down within short periods of time for no explainable reason. These

are often an indication of a gross laboratory or sampling error rather than just inaccurate

results.

5.4.1 Influence of Factors Affecting Transformer Reliability

Typical values for hydrocarbons (except C2H

2) are markedly higher in power

transformers of the shell-type and in shunt reactors than in the mostly core-type

transformers, possibly because they operate at higher temperatures. Typical values in

instrument transformers are much lower than in power transformers. Typical values are

higher in the early years of the transformers, suggesting that some unstable chemical

bonds in the paper or oil insulation are broken in the early years, then the remaining ones

are more stable afterwards. Typical values are also slightly higher for faults in oil than in

paper. But typical values are not dependent on oil volume, suggesting that smaller

amounts of gases (and smaller faults) are formed in smaller equipment.

5.4.2 Calculation of Own Typical Values

Since typical values are influenced by such factors as transformer age and type and

loading practices, each individual network is encouraged to calculate the typical values

corresponding to its own transformer population. This can be done easily by listing DGA

results by increasing order of values, for each of the fault gases (e.g., H2). The value

corresponding to 90 % of the cumulative number of DGA analyses is the 90 % typical

value. For example, 90 % of H2

values in the transformer population of the network are

below this typical value, and 10 % (the upper percentile) are above. This can be done for

both concentrations values and rates of gas increase (RGI). By default, if typical values

cannot be calculated, for example because of an insufficient DGA data bank, the typical

gas concentration levels and rates of increase reported in various countries by CIGRE and

the IEC may be used as a rough approximation Table 5.2 and Table 5.3.

Table 5.2: Ranges of 90% typical values for power transformer in ppm

C2H2 H2 CH4 C2H4 C2H6 CO CO2

All Transformers 50- 30- 60- 20-90 400-600 3800-

99

150 130 280 1400

No OLTCs 2-20

Communicating

OLTC

60-280

Table 5.3: Ranges of 90% typical rate of gas increase for power transformer in ppm/year

C2H2 H2 CH4 C2H4 C2H6 CO CO2

All Transformers 35-132 10-120 32-145 5-90 260-

1060

1700-10000

No OLTCs 2-20

Communicating

OLTCs

21-37

5.4.3 Adjustment of Pre-failure and Alarm Values

Pre-failure concentration values and pre-failure rates of gas increase can be obtained by

calculating the probability of having a failure-related event (PFS, in %) in a transformer

in service, as a function of gas concentration level in oil. This is done by calculating the

following ratio, for each individual gas, at different concentrations. Thus number of DGA

analyses followed by an event such as tripping, tank rupture, fire or explosion, divided by

the total number of analyses.

In Fig. 5.6, the PFS value is indicated as a function of the concentration of C2H

2, in power

transformers without communicating OLTCs [70]. It can be seen that even at low

concentration values (near the 90 % typical value of 5 ppm), the PFS is not zero but

around 12 %. In such cases, a fault probably developed in the transformer very rapidly

after the DGA analysis, without advanced warning. Above a value of around 350 ppm,

there is an inflexion point in the curve above which the PFS increases rapidly.

PFS, in %

Figure 5.6: Probability of having a failure-related event(PFS, In %) as a function of C2H2

concentration in service of in ppm and norm in %

This corresponds approximately to the 99 % typical value and to 1 % of DGA analyses,

which is not far from the annual failure rate of transformers (0.3 %). This value has been

100

defined as the pre-failure gas concentration value (PFGC). The PFGC values observed for

the other gases are indicated in Table 5.4.

Table 5.4: Pre-failure gas concentration values at CIGRE for core- type transformers

H2 CH4 C2H6 C2H4 C2H2 CO

550-1320 340-460 750-1050 700-990 310-600 980-3000

By combining pre-failure values and actual rates of increase in service, one may have an

idea of how long it may take to reach failure (if rates do not accelerate), and plan

appropriate actions.

Alarm gas concentration values may be defined as the values corresponding to x times the

PFGC population. For example, if x = 2, the alarm value corresponds to the 98 % typical

value, or 170 ppm. Alarm values thus calculated for the other gases can be found in.

5.5 APPLICATION OF DUVAL TRIANGLE METHOD 1 (DTM 1)

There are two different procedures to use DTMs as follows;

1. By using total accumulated gas

2. By using total increase in gases between conjugative samples

For this thesis work, total accumulated gas based procedure for the DGA using DTM 1

has been adopted.

5.5.1 Graphical Fault Interpretation

Graphical DGA fault diagnosis by Duval triangle 1 is carried out as follows; Consider the

three side of triangle in triangular coordinates (x, y and z) representing the relative

proportion of CH4, C2H4 and C2H2, from 0% to 100% for each gas. Numerical boundary

zones for seven key faults are shown in Table 5.5. To find the faults graphically (manual),

first calculate the percentage of each gas as per above equations (5.1 - 5.3). Then draw the

lines % CH4 quantity parallel to C2H2 line, % C2H4 quantity parallel to CH4 line and

%C2H2 quantity parallel to CH4 as on the specially supplied graphical sheets shown in

Fig. 5.2. Key Faults along with boundary zones are presented of Duval Triangle method1

in Table 5.5.

Table 5.5 Numerical zone boundaries

No. Fault Limits of zones

1 PD 98 % CH4 2 D1 23 % C2H4 13 % C2H2

3 D2 23 % C2H4 13 % C2H2 40 % C2H4 29 % C2H2

4 DT 40% C2H4 50% C2H4 04% C2H2 13% C2H2 15% C2H2

5 T1 04% C2H2 20 % C2H4

6 T2 04% C2H2 10 % C2H4 50 % C2H4

7 T3 15% C2H2 50 % C2H4

101

Thus drawn intersection of all three lines would indicate the fault responsible for the

DGA results in the transformer. Such verification of faults by Duval triangle method

(manual) DGA has been done (fault reported transformers). These results were verified

with DGA interpretation for total dissolved combustible gases by other procedures used

by different utilities in INDIA.

Example: CH4= 56 ppm, C2H4= 55 ppm and C2H2= 43 ppm, graphically (manually)

calculated result D2 displayed in Fig. 5.7.

Figure 5.7: Graphical analyses on DTM 1

5.5.2 Simulated Fault Interpretation with DTM 1

Following steps are used for simulated fault interpretations;

Step 1; In this research work, firstly, polygon coordinates for the numerical zone

boundaries of seven key faults of DTM 1 have been generated in terms of percentages of

CH4, C2H4 and C2H2, from 0% to 100% respectively shown in Table 5.6.

Table 5.6: Triangular coordinates for DTM 1 zones

No. Area Points %CH4 %C2H4 %C2H2

1 PD PD1 98 2 00

2 PD2 100 00 00

3 PD3 98 00 2

4 D1 D11 0 0 100

5 D12 0 23 77

6 D13 64 23 13

7 D14 87 00 13

102

Step 2; A flow-chart for simulation of fault interpretations developed for DTM 1 on

MATLAB 7.6 is presented in Fig. 5.8. To define each polygon, the defined points are

converted to Cartesian coordinates for percentage of gases for types of fault. Same inputs,

which are used for graphical (Manually) fault interpretations in Step 1 analyzed by DTM

1simulation on MATLAB-7.10 and revealed the same result D2 represented in Fig. 5.9.

The DGA fault analysis on simulated DTM 1 for the available fault reports are thoroughly

carried out for all the samples (reports) on MATLAB 7.10 and cross verified. All the

8 D2

D21 00 23 77

9 D22 0 71 29

10 D23 31 40 29

11 D24 47 40 13

12 D25 64 23 13

13 DT DT1 00 71 29

14 DT2 00 85 15

15 DT3 35 50 15

16 DT4 46 50 4

17 DT5 96 00 4

18 DT6 87 00 13

19 DT7 47 40 13

20 DT8 31 40 29

21 T1 T11 76 20 4

22 T12 80 20 00

23 T13 98 2 00

24 T14 98 00 2

25 T15 96 00 4

26 T2 T21 46 50 4

27 T22 50 50 00

28 T23 80 20 00

29 T24 76 20 4

30 T3 T31 00 85 15

31 T32 00 100 00

32 T33 50 50 00

33 T34 35 50 15

103

comparative fault analysis between manual and software implementation of DTM 1 along

with the fault analysis with other diagnostic techniques (faults analyzed by the respective

authorities and utilities) are prepared and a comparative study carried out and a complete

Table 5.28.

1

Figure 5.8: Flow-chart of DTM 1

CH4>120

C2H4>50

C2H2>1

Take gas levels of

C2H2, C2H4, CH4

Define the boundaries of the faults

acc. to the Duval Triangle 1.

Calculate the percentage of

C2H2,C2H4,CH4 as p1,p2,p3

p1>=98

DUVAL TRIANGLE TEST IS NOT

APPLICABLE

No

Yes

THERMAL FAULT

> 700 0C -- T3

DISCHARGE OF LOW

ENERGY -- D1

THERMAL FAULT <

300 0C -- T1

THERMAL FAULT 300-

700 0C -- T2

PARTIAL DISCHARGE --

PD

DISCHRAGE OF

HIGH ENERGY -- D2

p2 20-50 p3<4

P3<15

p2<23 p3<13

p2<20

p2>=50

p2 23-40 p3 29-77

No

No

No

No

No

Yes

Yes

No

Yes Yes

Yes

Yes

Yes

Yes

Yes

No THERMAL AND ELECTRICAL

FAULT - DT

Start

Stop

Stop

Stop

Stop

Stop

Stop

Stop

Stop

104

By comparing the graphical and simulated DTM 1 based analysis results are obtained,

shown as Fig. 5.9.

Figure 5.9: Duval Triangle method 1 simulation analysis

Step 3; Two other conventional methods: Dornenburg‘s and Roger‘s diagnostic

techniques are developed. A generalized flow-charts for fault analysis through these two

diagnostic tools are given in Fig. 4.25 and Fig.4.26. All the comparative fault analysis

between manual and simulation implication of DTM 1 along with the fault analysis for

Dornenburg and Roger‘s diagnostic techniques (faults analyzed by the respective

authorities and utilities) are prepared and given in Annexure-. It is found that DTM1

fault diagnosis on power transformers gives better fault interpretation as compared to

Dornenburg‘s ratios and Roger‘s ratios fault interpretations.

5.6 DUVAL TRIANGLE METOD 2 FOR LOAD TAP CHANGERS OF THE

OIL TYPE

The normal operation of load tap changers (LTCs) of the oil type involves arcing in the

oil. The ―Duval Triangle 2 for LTCs of the oil type‖, taking this into account in a more

user-friendly manner is presented in Fig. 5.10. Zone boundaries in the Duval Triangle 2

for LTCs of the oil type are based on the basis of examples of inspected cases of faults

indicated in Fig. 5.11. DGA results and references for these inspected cases are given in

Table 5.7. However, the N zone may be smaller for some individual types or brands of

LTCs of the oil type, and its actual boundaries can be determined by using DGA results

from such individual units which have been newly installed or where normal operation is

ascertained. Fault diagnoses should not be attempted in LTCs if concentrations of C2H2

and C2H4 are < 10 ppm, because of possible contamination and uncertainty of laboratory

results.

105

Figure 5.10: The Duval triangle 2 for LTCs of Oil Type Transformers

Figure 5.11: Inspected Cases of Faults in the Duval Triangle 2 for OLTCs of Oil-type

Transformers

The identification of fault zones in Fig. 5.10 and Fig. 5.11 is indicated in Table 5.7. In the

second column of Table 5.7, the types of faults mentioned are: D1 = Discharges of low

energy; D2 = Discharges of high energy; T2 = Thermal faults of temperature 300 ºC < T

<700 ºC; T3 = Thermal faults of temperature > 700 ºC.

Table 5.7: Identification of fault zones in above OLTC figure

No. Zone Identification Recommended actions

1 N Normal operation

2 D1 Abnormal arcing D1(outside of N

zone)

Inspect the LTC for small sign

of arcing.

3 X1 Abnormal arcing D1 or thermal

fault in progress

Still area is under

investigations.

4 X3 Fault T3 or T2 in progress with Test or inspect LTC for sign of

106

light coking or increased resistance

of contacts. Or severe D2 arcing

light coking or resistance of

contacts, or of severe arcing.

5 T2 Severe thermal fault T2

(300<T<700oC), coking

Change the oil.

Inspect the LTC for coking of

contacts.

6 T3 Severe thermal fault T3

(T>700oC), heavy coking

Numerical zone boundaries in Fig. 5.10 and Fig. 5.11 are indicated in Table 5.8. The

numerical zone boundaries indicated in Table 5.8 for the N (normal operation) zone cover

all types of LTCs of the oil type and may be used by default.

Table 5.8: Numerical zone boundaries of Duval triangle method 2

No. Fault Limits of Zones (%)

N CH4= 19, C2H4=23, CH4=2, C2H4=6

D1 CH4= 19, C2H4=6, CH4=2, C2H4=23

X1 CH4= 19, C2H4=23

X3 C2H4=23, C2H2=15

T2 C2H4=23, C2H2=15, C2H4=50

T3 C2H4=50, C2H2=15

5.6.1 Implementation and Results from Duval triangle method 2 (DTM 2) for

OLTCs

Here, with the help of reconstructed numerical zone boundaries of faults for DTM 2 is

reconstructed and shown in Table 5.9. Further Table 5.10 shows the polygon coordinates

of DTM 2 fault ranges.

Table 5.9: Numerical zones of DTM 2

No. Fault Limit of zones

1 N 19 % CH4 23% C2H4 2% CH4 6% C2H4

2 D1 19 % C2H4 6 % C2H2 2 %CH4 23% C2H4

3 X1 19 % C2H2 23% C2H4

4 X3 23% C2H4 15% C2H2

5 T2 2% C2H4 15 % C2H2 502H4

6 T3 50 % C2H4 15 % C2H4

107

Table 5.10: Triangular coordinates for Duval triangle method 2 zones


1 N N1 19 6 75

2 N2 19 23 58

3 N3 2 6 92

4 N4 2 23 75

5 D1 D11 19 0 81

6 D12 19 6 75

7 D13 2 6 92

8 D14 2 23 75

9 D2 D15 0 23 77

10 D16 0 0 100

11 X1 X11 19 0 81

12 X12 100 0 0

13 X13 77 23 0

14 X14 19 23 58

15 X3 X3.1 0 23 77

16 X3.2 62 23 15

17 X3.3 0 85 15

18

T2

T2.1 62 23 15

19 T2.2 77 23 0

20 T2.3 50 50 0

21 T2.4 35 50 15

22 T3 T31 35 50 15

23 T32 50 50 0

24 T33 0 100 0

25 T34 0 85 15

Based on numerical zone boundaries and developed polygon coordinates, a simulation on

MATLAB is developed. The result of one of the sample for input given data is represented

in Fig. 5.12.

Enter the input data--

108

Figure 5.12: Fault analysis by DTM 2 for OLTC

5.7 THE DUVAL TRIANGLES METHOD 3 FOR NON-MINERAL OILS

Mineral oils are the most widely used insulating fluid in electrical equipment. However,

other non-mineral oils are increasingly used because they are less flammable or more

environmentally friendly.

This has raised the question of whether new DGA interpretation methods are needed for

them, or if existing methods used for mineral oils can be adjusted for non-mineral oils.

Very few inspected cases of faults in equipment filled with non-mineral oils are available,

because these fluids have been used only for a small number of years and in a limited

quantity of equipment. To evaluate gas formation patterns in these fluids, therefore, these

tests are simulating faults in the laboratory, and performed in parallel on both mineral oils

and the new fluids.

A number of such test results have been published and are readjusted to form the new

zone boundaries for silicone oil, synthetic ester (Midel), and natural esters/ vegetable oils

(FR3 and BioTemp), using the Duval Triangle 1 for transformers DGA results were

obtained either by the laboratory gas extraction method of Head Space (and were

109

corrected for the different Ostwald coefficients of the non-mineral oil ), or by the Toepler

or Partial Degassing methods (and do not need significant corrections).

The differences in gas formation patterns observed by Duval triangle method 1 between

mineral and non-mineral oils can be used to adjust zone boundaries of the Duval Triangle

1 and to create a Duval Triangle 3 for each of the non-mineral oils, as indicated in Fig.

5.13.

The reason for the different gas patterns of mineral and non-mineral oils can only be

speculative at this stage and is still under investigations. These differences are probably

related to the differences in chemical structure of the oils. For instance, the carbon-

oxygen bonds in natural and synthetic esters (FR3, BioTemp and Midel), and the silicon-

oxygen bonds in silicones, may slightly affect the chemical strength of neighboring

carbon-carbon and carbon-hydrogen bonds, and might explain the different relative

amounts of hydrocarbon gases formed at the same levels of fault energy or temperature.

The two natural esters (vegetable/seed oils) examined based on saturated/unsaturated

oleic/fatty acids [13], and the synthetic ester on pentaerythritol. Non-mineral oils with

different chemical structures may result in different gas patterns and zone boundaries.

Figure 5.13: Duval Triangle 3 for Non-mineral oils with their zone boundaries in colours

110

Zone boundaries of the Duval Triangle method 3 for non-mineral oils (Silicone, Midel,

FR3 and BioTemp) are the same as those of the Duval triangle method 1 for mineral oils

except for the boundaries (in % C2H4) between zones D1/ D2, T1/ T2, and T2/ T3, as

indicated in Fig. 5.13 and Table 5.11. The boundary between faults D2 and DT may also

have been modified when actual cases of faults D2 (high-energy arcing) are reported in

transformers in service filled with these non-mineral oils.

Table 5.11: Zone Boundaries (% C2H4) for Duval triangle method 3 (non-mineral oils)

Boundary

between

zones

Mineral

oils

Silicone Midel BioTemp FR3

D1/D2 23 9 26 20 25

T1/T2 20 16 39 52 43

T2/T3 50 (46) (68) (82) 63

5.7.1 Implementation and results from Duval triangle methods 3 (DTMs 3) for Non-

mineral oils

Simulation for all four Duval triangle method 3 are as follows:

5.7.1.1 Silicone oil

With the help of its numerical zone boundaries as shown in Table 5.12, whose polygon

coordinates are created and shown in Table 5.13. Therefore, the simulation for DTM 3,

silicone oil is developed.

Table 5.12: Numerical zone boundaries for DTM 3 (Silicone oil)


1 PD 98 % CH4 2 D1 10 % C2H4 12 % C2H2

3 D2 10 % C2H4 12 % C2H2 40 % C2H4 28 % C2H2

4 DT 40% C2H4 46% C2H4 05% C2H2 12% C2H2 28% C2H2

5 T1 05% C2H2 16 % C2H4

6 T2 05% C2H2 16 % C2H4 46 % C2H4

7 T3 14% C2H2 46 % C2H4

Table 5.13: Triangular coordinates for DTM 3 zones (Silicone Oil)

No. Area Points CH4 C2H4 C2H2

1 PD PD1 98 2 0

2 PD2 100 0 0

3 PD3 98 2 0

111

4 D1 D11 0 0 100

5 D12 88 0 12

6 D13 78 10 12

7 D14 0 10 90

8 D2 D21 0 10 90

9 D22 78 10 12

10 D23 48 40 12

11 D24 32 40 28

12 D25 0 72 28

13 DT DT1 0 72 28

14 DT2 32 40 28

15 DT3 48 40 12

16 DT4 88 0 12

17 DT5 95 00 5

18 DT6 49 46 5

19 DT7 40 46 14

20 DT8 0 86 14

21 T1 T11 95 0 5

22 T12 98 0 2

23 T13 98 2 0

24 T14 84 16 0

25 T15 79 16 5

26 T2 T21 84 46 0

27 T22 79 16 5

28 T23 49 46 5

29 T24 54 46 0

30 T3 T31 54 46 0

31 T32 40 46 14

32 T33 0 86 14

33 T34 0 100 0

The fault analysis result for one of the sample of below given data is analyzed and

represented in Fig. 5.14. This shows type of fault as well as the graphical representation

of the fault in simulation.

112

Figure 5.14: Fault analysis results of DTM 3 for non-mineral oil (Silicon oil)

5.7.1.2 Midel oil

Similarly, numerical zone boundaries of DTM3, Midel oil are shown in Table 5.14 and

polygon coordinates are created for these zone boundaries are given in Table 5.15.

Table 5.14: Numerical zone boundaries for DTM 3 (Midel oil)


1 PD 98 % CH4 2 D1 28 % C2H4 12 % C2H2

3 D2 28 % C2H4 12 % C2H2 42 % C2H4 27 % C2H2

4 DT 40% C2H4 64% C2H4 05% C2H2 14% C2H2 12% C2H2

5 T1 05% C2H2 40% C2H4

6 T2 05% C2H2 40 % C2H4 69 % C2H4

7 T3 14% C2H2 69 % C2H4

Table 5.15: Triangular coordinates for DTM 3 zones (Midel Oil)


1 PD PD1 98 2 0

2 PD2 100 0 0

3 PD3 98 2 0

4 D1 D11 0 0 100

5 D12 88 0 12

6 D13 60 28 12

7 D14 0 28 72

8 D2 D21 0 28 72

9 D22 60 28 12

10 D23 48 40 12

11 D24 32 40 28

113

12 D25 0 72 28

13 DT DT1 88 0 12

14 DT2 95 0 5

15 DT3 26 69 5

16 DT4 17 69 14

17 DT5 00 86 14

18 DT6 00 72 28

19 DT7 32 40 28

20 DT8 0 86 14

21 T1 T11 95 0 5

22 T12 98 0 2

23 T13 98 2 0

24 T14 60 40 0

25 T15 55 40 5

26 T2 T21 55 40 5

27 T22 60 40 00

28 T23 31 69 00

29 T24 26 69 14

30 T3 T31 31 69 0

31 T32 17 69 14

32 T33 0

86 14

33 T34 0 100 0

The three inputs from a sample are entered in the simulation environment. The fault

diagnosis results by DTM3 Midel oil are shown in words as sell as graphical analysis

shown in Fig. 5.15.

enter the value of Methane=23

enter the value of Ethylene=12

enter the value of Acetylene=65

"Duval Triangle Test is Applicable"

""""""DUVAL TRIANGLE RESULTS"""

DISCHARGE OF LOW ENERGY -- D1

114

Figure 5.15: Fault analysis results by DTM3 (Midel oil)

5.7.1.3 FR 3 oil

In a similar way, numerical zone boundaries of DTM3, FR3 natural ester oil is shown in

Table 5.16 whose polygon coordinates are developed and shown in Table 5.17.

Table 5.16: Numerical zone boundaries for DTM 3 (FR3 oil)


1 PD 99 % CH4

2 D1 25 % C2H4 13 % C2H2

3 D2 25 % C2H4 13 % C2H2 40 % C2H4 19 % C2H2

4 DT 40% C2H4 64% C2H4 05% C2H2 13% C2H2

29% C2H2

5 T1 05% C2H2 44% C2H4

6 T2 05% C2H2 44 % C2H4 64 % C2H4

7 T3 14% C2H2 65 % C2H4

Table 5.17: Triangular coordinates for DTM 3 zones (FR3 Oil)

No. Area Points CH4 C2H4 C2H2

1 PD PD1 99 0 1

2 PD2 100 0 0

3 PD3 99 1 0

4 D1 D11 0 0 100

5 D12 87 0 13

6 D13 62 25 13

7 D14 0 25 75

8 D2 D21 0 25 75

9 D22 62 25 13

10 D23 47 40 13

11 D24 41 40 29

12 D25 0 71 29

115

13 DT DT1 87 0 13

14 DT2 95 0 5

15 DT3 31 64 5

16 DT4 12 64 14

17 DT5 00 86 14

18 DT6 00 71 29

19 DT7 41 40 29

20 DT8 41 40 13

21 T1 T11 95 0 5

22 T12 99 0 1

23 T13 99 1 0

24 T14 56 44 0

25 T15 51 44 5

26 T2 T21 51 49 5

27 T22 56 44 00

28 T23 36 64 00

29 T24 31 64 5

30

T3

T31 36 64 0

31 T32 22 64 14

32 T33 0

86 14

33 T34 0 100 0

A sample of below given inputs is tested to find the type of fault though DTM3 FR3 oil.

The analysis results are shown in simulated DTM3 FR3 Fig. 5.16 and as written fault in

words.






DISCHRAGE OF HIGH ENERGY -- D2

116

>>

Figure 5.16: Fault analysis results by DTM3 (FR3 oil)

5.7.1.4. Bio-Temp oil

Further, numerical zone boundaries of Bio-Temp oil are given in Table 5.18 and polygon

coordinates for the same boundaries of BioTemp oil are developed and shown in Table

5.19.

Table 5.18: Numerical zone boundaries for DTM 3 (BioTemp oil)


1 PD 98 % CH4 2 D1 10 % C2H4 12 % C2H2

3 D2 10 % C2H4 12 % C2H2 40 % C2H4 28 % C2H2

4 DT 40% C2H4 46% C2H4 05% C2H2 12% C2H2 28% C2H2

5 T1 05% C2H2 16 % C2H4

6 T2 05% C2H2 16 % C2H4 46 % C2H4

7 T3 14% C2H2 46 % C2H4

Table 5.19: Triangular coordinates for DTM 3 zones (BioTemp oil)


1 PD PD1 98 0 2

2 PD2 100 0 0

3 PD3 98 2 0

4 D1 D11 0 0 100

5 D12 89 0 11

6 D13 68 21 11

7 D14 0 21 79

8 D2 D21 0 21 79

9 D22 68 21 11

10 D23 48 41 11

11 D24 31 41 28

117

12 D25 0 72 28

13 DT DT1 89 0 11

14 DT2 97 0 3

15 DT3 13 84 3

16 DT4 00 89 11

17 DT5 31 41 28

18 DT6 48 41 11

19 T1 T11 97 0 3

20 T12 98 0 2

21 T13 98 2 0

22 T14 47 53 0

23 T15 44 53 3

24 T2 T21 44 53 3

25 T22 47 53 00

26 T23 16 84 00

27 T24 13 84 3

28 T3 T31 0 84 16

29 T32 0 16 84

30 T33 0

100 00

Here a sample for fault diagnosis purposes tested for DTM3 BioTemp oil as below given

inputs. The fault diagnosis results from simulated MatLab environment in graphical

presentation shown in Fig, 5.17 and also in words.






DISCHRAGE OF HIGH ENERGY -- D2

Figure 5.17 Fault analysis result by DTM 3 (BioTemp oil)

118

5.8 DUVAL TRIANGLE METHODS 4 AND 5 FOR LOW-TEMPERATURE

FAULTS IN TRANSFORMERS

Duval triangle method 1 for transformers has proved quite efficient to determine the

general type of fault occurring in transformers in service (PD, D1, D2, T1, T2, T3, DT). It

is based on the use of 3 gases (CH4, C2H4 and C2H2) representative of low-, high-, and

very high-energy or temperature faults, respectively [64]. When DGA results are close to

the boundary between zones PD and T1. However, manually it may be difficult to

distinguish between these two types of faults. Also, some types of oils on the market tend

to be ―stray gassing‖, i.e., to unexpectedly form gases at relatively low temperatures such

as 80 °C to 200 °C. Stray gases may appear in the PD, T1, or T2 zones, and therefore may

interfere with the identification of these faults in transformers.

The Duval Triangles 4 and 5 for low-temperature faults in transformers have been

developed to remove these uncertainties. They should be applied only to faults identified

by the Duval Triangle 1 as possible faults PD, T1, or T2, and should be viewed only as a

complement of information for the Duval Triangle 1. These should not be applied to

faults identified by the Duval Triangle 1 as faults D1, D2, or T3.

5.8.1 Duval Triangle Method 4 for Low-Energy Gases

The Duval Triangle method 4 (DTM 4) uses the 3 low-energy gases (H2, CH4, and C2H6)

for incipient fault diagnosis of transformers filled with mineral oils in the main tank.

DTM 4 is shown in Fig.5.18.

Figure 5.18: Duval triangle method 4 transformers filled with mineral oil

The identification of fault zones in DTM 4 is indicated in Table 5.20 and the numerical

zones boundaries (in %) in Table 5.21.

119

Table 5.20 Duval triangle method 4 fault zones identifications

No. Zones Identification

1 PD Corona Partial Discharge

2 S Stray gassing of mineral oil

3 C Hot spot with carbonization of paper (T > 300oC)

4 O Overheating (T <250oC)

Table 5.21 Duval triangle 4 numerical zone boundary

No. Fault Limits of Zones

1 PD CH4=2, C2H6=1, CH4=15

2 S C2H6=44, H2=9, C2H6=24, CH4=36, CH4=15,

C2H6=1, CH4=2

3 C CH4=36, C2H6=24

4 O H2=9, C2H6=24

5.8.2 Implementation and Results for Duval Triangle Method 4

From the fault zone limits of DTM4, the percentage numerical zone boundaries are

represented in Table 5.22 and DTM4‘s polygon coordinates are created, shown in Table

5.23.

Table 5.22: Numerical zone boundaries for DTM 4 (low intensity faults)

No. Fault Limits of Zones%

1 PD 2 CH4 C2H6 15 CH4

2 S 44 C2H6 19 CH4 24 C2H6 36 CH4 15 CH4 1 C2H6 2 CH4

3 C 36 CH4 24 C2H6

4 O 9 H2 24 C2H6

Table 5.23: Triangular coordinates for DTM 4 zones

No. Area Points % H2 CH4%

CH4

% C2H6

1 PD PD1 98 2 0

2 PD2 85 15 0

3 PD3 84 15 1

4 PD4 93 2 1

5 S S1 56 0 44

6 S2 100 0 0

7 S3 98 2 0

8 S4 97 2 1

9 S5 84 15 1

120

10 S6 64 36 0

11 S7 74 26 0

12 S8 42 36 24

13 S9 9 67 24

14 C C1 0 76 24

15 C2 40 36 24

16 C3 0 100 0

17 C4 0 76 24

18 O O1 0 0 100

19 O2 9 0 81

20 O3 9 69 24

21 O4 0 76 24

The inputs of a sample are entered to simulate the DTM4 in MATLAB environment and

the fault diagnosis in DTM4 simulated results are shown in Fig.5.19.

Enter the input data—

Figure 5.19: Fault analysis results by DTM 4 for low-intensity faults

5.8.3 Duval Triangle Method 5 (DTM5) for Low-Temperature Faults

It is seen that gases formed by corona partial discharges PD, and low-temperature faults

in oil are relatively similar. This can be explained, however, by the fact that temperature

121

in such PDs, occurring in the gas phase and also known as cold plasma discharges, is

indeed quite low compared to sparking partial discharges of the D1 type.

The Duval triangle method 5 for low-temperature faults in transformers can be used to

confirm fault attributions which are still uncertain after using the Duval triangle method

4. It uses the 3 so-called temperature gases (C2H4, CH4, and C2H6) and is presented in

Fig.5.20. It is based on the inspected cases of transformer faults and stray gassing

laboratory test results. References for these inspected cases are the same as in low-energy

gases for T3 faults.

Figure 5.20: Duval triangle method 5 for transformers filled with mineral oil (Low-

Temperature Gases)

The identification of fault zones of DTM5 is indicated in Table 5.24 and the numerical

zones boundaries in Table 5.25.

Table 5.24: Duval triangle method 5 fault zones identifications

No. Fault Identification

1 PD Corona Partial Discharge

2 S Stray gassing of mineral oil

3 C Hot spot with carbonization of paper (T > 300oC)

4 O Overheating (T <250oC)

5 T3 Thermal faults of very high temperatures(T > 700oC)

Table 5.25: Duval triangle method 5 numerical zone boundaries

No. Fault Limits of Zones

1 PD C2H4=1, C2H6=15,

122

2 S C2H6=53, C2H4=10, C2H6=15,

3 C C2H4=36, C2H6=30, C2H4=35,

4 O C2H4=10, C2H6=53 C2H6=15, C2H4=1,

5 T3 C2H4=34,

Faults occurring in zone O are only evidenced by paper turning brown and are often

difficult to find by visual inspection of the oil-impregnated paper. Cases through Duval

triangle method 4 & 5 in are also located in zone O, and have been attributed to stray

gassing. This suggests that temperature in these cases possibly was > 200°C. New stray

gassing tests at 250 to 300 °C would be necessary to confirm this possibility.

Stray gassing of non-mineral oils (Midel, FR3, and BioTemp), based on laboratory

results, occurs in the Duval triangle method 4 in zone S and in the unnamed zone between

zones S and O. It occurs in the Duval triangle method 5 in zone O and in the unnamed

zone between zones O and C.

Only a few cases of abnormal gassing in transformers in service filled with FR3 oil have

been available so far. They occur mostly in zones O and S of the Duval triangle 4 and in

zone O of the Duval triangle 5.

5.8.4 Implementation and Results for Duval Triangle Method 5 (DTM 5)

Similar analysis is carried out for DTM 5. Its percentage numerical zone boundaries are

shown in Table 5.26, whose polygon coordinates are created and shown in Table 527.

Table 5.26: Numerical zone boundaries for DTM 5

No. Faults Limits of Zones

1 PD 1% C2H4 15% C2H6

2 S 53% C2H6 10% C2H4 13% C2H6

3 C 10% C2H4 30% C2H6 36 C2H4

4 O 10% C2H4 53% C2H6 15% C2H6

5 T3 36% C2H4

Table 5.27 Triangular coordinates for DTM 5 zones

No. Area Points % CH4 % C2H4 % C2H6

1 PD PD1 85 0 15

2 PD2 99 0 1

3 PD3 98 1 1

4 PD4 84 15 1

5 S S1 47 0 53

6 S2 85 0 15

123

7 S3 75 10 15

8 S4 37 10 53

9 O O1 0 0 100

10 O2 47 0 53

11 O3 37 10 53

12 O4 0 10 90

13 O5 85 0 15

14 O6 84 15 1

15 O7 75 10 15

16 O8 85 10 0

17 O9 100 0 0

18 O10 99 0 1

19 O11 98 1 1

20 C C1 60 10 30

21 C2 34 36 30

22 C3 64 36 0

23 C4 90 10 0

24 T3

T31 0 37 63

25 T32 64 36 0

26 T33 0 100 0

Simulated result from DTM5 for a given sample input in fault interpretation of power

transformer is shown in Fig. 5.21.

Figure 5.21: Fault analysis results by DTM 5 for low temperature fault

124

Duval triangle method 1 to Duval triangle method 5 can be applied as DGA for fault

diagnosis of power transformers, whether oil in main tank, oil in OLTCs, different types

of synthetic oils and also separately for Low-energy faults. Duval triangle methods can

provide complete solutions. A generalized flow- chart for complete solution is shown in

Fig. 5.22.

Figure 5.22: Generalized flow-chart for complete solution by Duval triangle methods

SUMULATE DTM 5

Start

TYPE OF OIL? SIMULATE DTM 3

CHECKING FOR

WHICH OIL? SIMULATE DTM 2

AMBIGUIS

FAULTS?

SIMULATE DTM 4

SIMULATE DTM 1

AMBIGUIS

FAULTS? SIMULATE DTM 5

SIMULATE DTM 4

Stop Stop Stop Stop

OLTC

MAIN TANK

SYNTHETIC

MINERAL

LOW INTENSITY

HIGH INTENSITY

LOW INTENSITY

HIGH INTENSITY

125

The following conclusions are drawn from the DTMs:

Duval triangle fault interpretation methods can be used graphically in a very easy

way and the evaluation of faults is also easy.

Available eight Duval triangles are capable of fault diagnosis separately; such as

main transformer, online load tap-changers (OLTCs), faults with low energy,

faults with low temperatures and synthetic oils.

Simulated fault analysis results in DTM1 compared with manual fault diagnosis of power

transformers for the reported faults are shown in Table 5.28 by different conventional

fault interpretations method used across the world. Same simulated results are compared

with Roger‘s ratio and Dornenburg‘s ratio fault interpretations techniques to emphasize

the advantages of Duval triangle method fault interpretation are given in Table 5.29.

These fault analyses tables have been are prepared from the fault reports presented in

Appendix-B, collected from different authorities and organizations.

126

Table 5.28: Comparative analysis between simulated and graphical fault interpretations in Duval triangle 1

S.No. Transformer Rating GASSES

TOTAL %CH4 %C2H4 %C2H2 DGA Fault Diagnosis/ Comments DTM 1 RESULTS

MVA KV CH4 C2H4 C2H2 CEBG IEC, CIGRE IEEE/Reco. Comnts SIMULATION MANUAL

1 100 220/66 187 614 ND 801 23.35 76.65 ND C2H4 High Hot spot T3 T3

2 20 11/0.4 10 23 11 44 22.73 52.27 25.00 C2H2 High Overloading DT DT

3 9989 66-33/114 6852 3137 ND 9989 68.60 31.40 ND C2H2 High Old OIL T2 T2

4 100 220/66 254 313 417 984 25.81 31.81 42.38 C2H2 High Hot spot D2 D2

5 100 220/66 83 221 3 307 27.04 71.99 0.98 C2H4 High Hot spot T3 T3

6 315 400/200 55 72 0 127 43.31 56.69 0.00 300-700 °C >700°C T3 T3

7 315 400/220 79 89 0

1

68 47.02 52.98 0.00 300-700 °C <700°C

T3 T2

8 250 NG 547 530 nd 1077 50.79 49.21 ND 300-700 °C Cir. C T2 T2

9 25 NG 29 21 47 97 29.90 21.65 48.45 AD D1 D2

10 50 NG 748 2587 10 3345 22.36 77.34 0.30 AD T3 T3

11 50 400 108 50 0 158 68.35 31.65 0.00 Cir. C 300-700 °C Cir. C C2H4 High T2 T1

12 200 400/21 80 120 0 200 40.00 60.00 0.00 Cir. C >700°C Cir. C C2H4 High T3 T3

13 250 16/420 40 10 0 50 80.00 20.00 0.00

200-

300°C NF

200-300°C Ind. Gases D1 T1

14 200 400/21 90 125 0 215 41.86 58.14 0.00 Cir. C >700°C Cir. C C2H4 High T3 T3

15 250 16/420 30 0 0 30 100.00 0.00 0.00 <150°C 150-300°C 150°C C2H6 High PD PD

16 50 420 25 10 0 35 71.43 28.57 0.00 PD NF N SG T2 T1

17 63 420 105 15 0 120 87.50 12.50 0.00 PD NF N IG D1 T1

18 200 400/21 2 2 0 4 50.00 50.00 0.00 PD NF N SG T3 T2

19 200 420/21 1 1 0 2 50.00 50.00 0.00 PD NF N IG T3 T2

20 200 400/21 20 7 0 27 74.07 25.93 0.00 S OK NF N SG T2 T2

21 200 420/133 1 6 0 7 14.29 85.71 0.00 PD NF N NA T3 T3

22 200 420/33 31 10 0 41 75.61 24.39 0.00 S OK NF N NA T2 T2

23 240 21/420 4 1 0 5 80.00 20.00 0.00 S OK NF N NA T1 T2

127

24 80 132/11 0 0 0 0 nd nd nd PD NF N DT

25 200 21/1420 20 3 0 23 86.96 13.04 0.00 SOK NF N NA T1 T1

26 200 420/21 5 1 0 6 83.33 16.67 0.00 SOK NF N NA T1 T1

27 200 420/21 2 2 0 4 50.00 50.00 0.00 PD NF N IG T2 T2

28 80 132/11 10 1 0 11 90.91 9.09 0.00 SOK NF N NA T1 T1

29 75 400/345 8 5 0 13 61.54 38.46 0.00 SOK NF T2 T2

30 50 33/6.9 25 39 0 64 39.06 60.94 0.00 Cir. C 300-700 °C Cir. C C2H4 High T3 T3

31 25 11/6.65 3 0 0 3 100.00 0.00 0.00 SOK NF PD PD

32 25 11/6.6 15 0 0 15 100.00 0.00 0.00 SOK NF PD PD

33 50 420 5 1 0 6 83.33 16.67 0.00 DZD NF DZD B T1 T1

34 13 33/6.9 6 0 0 6 100.00 0.00 0.00 SOK NF N PD PD

35 32 33/6.9 8 3 0 11 72.73 27.27 0.00 SOK NF N T2 T2

36 50 33/6.9 17 33 0 50 34.00 66.00 0.00 MF <150°C MF T3 T3

37 16 16/6.9 26 2 0 28 92.86 7.14 0.00

200-

300°C NF 200-300°C T1 T1

38 25 220/6.9 5 13 0 18 27.78 72.22 0.00 CO <150°C CO T3 T3

39 13 33/6.9 5 10 0 15 33.33 66.67 0.00 SOK NF N T3 T3

40 16 15/6.9 3 0 0 3 100.00 0.00 0.00 PD NF N PD PD

41 25 16/6.9 26 2 0 28 92.86 7.14 0.00

200-

300°C NF 200-300°C T1 T1

42 63 145 85 3 0 88 96.59 3.41 0.00 SOK NF N T1 T1

43 13 33/6.9 7 0 0 7 100.00 0.00 0.00 SOK NF N PD PD

44 25 11/6.6 8 18 0 26 30.77 69.23 0.00 SOK NF N T3 T3

45 75 NG 14 0 0 14 100.00 0.00 0.00

45 75 NG 11 0 0 11 100.00 0.00 0.00

45 75 NG 1 0 0 1 100.00 0.00 0.00

45 75 NG 1 0 0 1 100.00 0.00 0.00

45 75 NG 3 0 0 3 100.00 0.00 0.00

128

45 75 NG 4 0 0 4 100.00 0.00 0.00

45 75 NG 5 0 0 5 100.00 0.00 0.00

45 75 NG 14 0 0 14 100.00 0.00 0.00 PD PD,D1,T1 N H2, CO2 PD PD

46 200 NG 160 35 0 195 82.05 17.95 0.00

47 16 NG 35 2 0 37 94.59 5.41 0.00

47 16 NG 32 1 0 33 96.97 3.03 0.00

47 16 NG 30 2 0 32 93.75 6.25 0.00

47 16 NG 30 1 0 31 96.77 3.23 0.00

47 16 NG 20 13 0 33 60.61 39.39 0.00

47 16 NG 10 5 0 15 66.67 33.33 0.00

47 16 NG 3 5 0 8 37.50 62.50 0.00

47 16 NG 56 55 43 154 36.36 35.71 27.92 N D1,D2 SRQ D2 D2

48 63 NG 395 475 0 870 45.40 54.60 0.00

48 63 NG 413 432 0 845 48.88 51.12 0.00

48 63 NG 403 412 0 815 49.45 50.55 0.00

48 63 NG 393 401 0 794 49.50 50.50 0.00

48 63 NG 494 361 0 855 57.78 42.22 0.00

48 63 NG 680 416 0 1096 62.04 37.96 0.00

48 63 NG 672 405 0 1077 62.40 37.60 0.00

48 63 NG 663 420 0 1083 61.22 38.78 0.00 >700°C T3,>700°C SQR >700°C T2 T2

49 50 NG 490 451 15 956 51.26 47.18 1.57

49 50 NG 185 510 18 713 25.95 71.53 2.52

49 50 NG 2100 1408 26 3534 59.42 39.84 0.74

49 50 NG 3010 1946 34 4990 60.32 39.00 0.68

49 50 NG 3586 2280 38 5904 60.74 38.62 0.64

49 50 NG 51 65 0 116 43.97 56.03 0.00

49 50 NG 36 110 3 149 24.16 73.83 2.01

129

9 50 NG 92 120 4 216 42.59 55.56 1.85 >700°C D2,T3 N H2, C2H4 T3 T3

50 16 132/33 2 8 0 10 20.00 80.00 0.00 T OH T3 T3

51 10 33/11 3 10 0 13 23.08 76.92 0.00 T N T3 T3

52 10 33/11 2 5 0 7 28.57 71.43 0.00 T N T3 T3

53 16 132/33 4 63 2 69 5.80 91.30 2.90 T OH T3 T3

54 16 132/33 4 9 0 13 30.77 69.23 0.00 T OH T3 T3

55 6.3 33/11 3 17 0 20 15.00 85.00 0.00 T OH T3 T3

56 6.3 33/11 2 8 0 10 20.00 80.00 0.00 T N T3 T3

57 16 132/33 45 110 147 302 14.90 36.42 48.68 T AD D2 D2

58 16 132/33 52 106 214 372 13.98 28.49 57.53 T AD D2 D2

59 16 132/33 1 18 0 19 5.26 94.74 0.00 T OH T3 T3

60 16/20 132/33 17 17 0 34 50.00 50.00 0.00 T OH T3 T3

61 16/20 132/33 1 12 0 13 7.69 92.31 0.00 T N T3 T3

62 16 132/33 3 64 42 109 2.75 58.72 38.53 OH OH DT D2

63 6.3 33/11 2 28 0 30 6.67 93.33 0.00 OH OH T3 T3

64 20 66/11 47 72 196 315 14.92 22.86 62.22 Cir. C& AD C2H4,C2H2 DT D2

65 20 66/11 51 79 201 331 15.41 23.87 60.73 Cir. C& AD C2H4,C2H2 D2 D2

66 20 66/11 45 70 189 304 14.80 23.03 62.17 Cir. C& AD C2H4,C2H2 D2 D2

67 20 66/11 49 75 199 323 15.17 23.22 61.61 Cir. C& AD C2H4,C2H2 D2 D2

DUVAL TRIANGLE fault diagnosis satisfy that more then 95% interpretations are correct compare to other fault diagnosis

techniques, those; CEBG, IEC CIGRE,IEEE, Roger‘s and Dornenburg‘s ratios, etc techniques are yet used by different agencies.

130

Table 5.29: DTM1 simulated fault diagnosis compared with Roger‘s and Dornenburg‘s ratio fault interpretations

S.

No.

Transformer Rating GASSES

TOTAL H2 C2H6 CO

DGA Fault Diagnosis/ Comments DTM 1

Simulated

Roger’s Dornen-

burg’s

MVA KV CH4 C2H2 C2H4 CEBG

IEC,

CIGRE IEEE/Reco. Comnts

1 100 220/66 187 ND 614 801 ND

178 ND C2H4 High Hot spot T3 >700C >700C

2 20 11/0.4 10 11 23 44 ND

2 ND C2H2 High Overloading DT NI NI

3 10/12.5 66-33/114 6852 ND 3137 9989 2195

3137 ND C2H2 High Old OIL T2 >700C TF

4 100 220/66 254 417 313 984 328

17 ND C2H2 High Hot spot D2 He Arc DA

5 100 220/66 83 3 221 307 13

10 ND C2H4 High Hot spot T3 >700C TF

6 315 400/200 55 0 72 127 39

22 80 300-700 °C >700°C

T3 >700C NF

7 315 400/220 79 0 89 168 40

42 110 300-700 °C <700°C

T3 <700C NF

8 250 NG 547 nd 530 1077 18

217 ND 300-700 °C

Cir. C T2 <700C TF

9 25 NG 20 47 21 88 139

10 ND AD D1 NI DA

10 50 NG 748 10 2587 3345 198

182 ND AD T3 >700C TF

11 50 400 108 0 50 158 42

Cir. C 300-700 °C Cir. C

C2H4 High T2 NI NF

12 200 400/21 80 0 120 200 45

Cir. C >700°C Cir. C

C2H4 High T3 NI NF

13 250 16/420 40 0 10 50 52 48 150 200-

300°C NF

200-300°C Ind. Gases D1 NF NF

14 200 400/21 90 0 125 215 50

30 95 Cir. C

>700°C Cir. C

C2H4 High T3 >700C TF

15 250 16/420 30 0 0 30 25

15 110 <150°C 150-300°C 150°C

C2H6 High PD NI NF

16 50 420 25 0 10 35 20

65 190 PD NF N SG T2

NI NF

17 63 420 105 0 15 120 45

66 310 PD NF N IG D1

NI TF

18 200 400/21 2 0 2 4 30 9 182 PD NF N SG T3

PD NF

19 200 420/21 1 0 1 2 0 1 50 PD NF N IG T3

<700C NF

20 200 400/21 20 0 7 27 48

5 90 S OK NF N SG T2

LTO NF

21 200 420/133 1 0 6 7 30

2 185 PD NF N

NA T3 NI NF

22 200 420/33 31 0 10 41 35

19 185 S OK NF N

NA T2 NF NF

131

23 240 21/420 4 0 1 5 35

5 232 S OK NF N

NA T1 NF NF

24 80 132/11 0 0 0 0 5 ND ND PD NF N DT NI NF

25 200 21/1420 20 0 3 23 45

3 230 SOK NF N NA T1 NF NF

26 200 420/21 5 0 1 6 10 1 90 SOK NF N NA T1 NI NF

27 200 420/21 2 0 2 4 36

3 47 PD NF N IG T2 PD NF

28 80 132/11 10 0 1 11 39 1 230 SOK NF N NA T1 NF NF

29 75 400/345 8 0 5 13 12

40 130 SOK NF T2 NF NF

30 50 33/6.9 25 0 39 64 16 19 70 Cir. C 300-700 °C Cir. C C2H4 High T3 <700C NF

31 25 11/6.65 3 0 0 3 22

0 36 SOK NF PD NI NF

32 25 11/6.6 15 0 0 15 33 0 160 SOK NF PD NI NF

33 50 420 5 0 1 6 83

0 105 DZD NF DZD B T1 NI NF

34 13 33/6.9 6 0 0 6 13 1 190 SOK NF N PD NI NF

35 32 33/6.9 8 0 3 11 22

1 210 SOK NF N T2 LTO NF

36 50 33/6.9 17 0 33 50 32 22 65 MF <150°C MF T3 LTO NF

37 16 16/6.9 26 0 2 28 36

45 192

200-

300°C NF 200-300°C T1 NF NF

38 25 220/6.9 5 0 13 18 30 10 380 CO <150°C CO T3 LTO NF

39 13 33/6.9 5 0 10 15 16

2 95 SOK NF N T3 NI NF

40 16 15/6.9 3 0 0 3 40 1 16 PD NF N PD NI NF

41 25 16/6.9 26 0 2 28 36

45 192

200-

300°C NF 200-300°C T1 NF NF

42 63 145 85 0 3 88 150


43 13 33/6.9 7 0 0 7 14

0 126 SOK NF N PD NI NF

44 25 11/6.6 8 0 18 26 22


45 75 NG 14 0 0 14

45 75 NG 11 0 0 11

45 75 NG 1 0 0 1

132

45 75 NG 1 0 0 1

45 75 NG 3 0 0 3

45 75 NG 4 0 0 4

45 75 NG 5 0 0 5

45 75 NG 14 0 0 14 41

0 47 PD PD,D1,T1 N H2, CO2 PD NI NF

46 200 NG 160 0 35 195 210 340

47 16 NG 35 0 2 37

47 16 NG 32 0 1 33

47 16 NG 30 0 2 32

47 16 NG 30 0 1 31

47 16 NG 20 0 13 33

47 16 NG 10 0 5 15

47 16 NG 3 0 5 8

47 16 NG 56 43 55 154 16 2 201 N D1,D2 SRQ D2 NI NI

48 63 NG 395 0 475 870

48 63 NG 413 0 432 845

48 63 NG 403 0 412 815

48 63 NG 393 0 401 794

48 63 NG 494 0 361 855

48 63 NG 680 0 416 1096

48 63 NG 672 0 405 1077

48 63 NG 663 0 420 1083 127 54 12 >700°C T3,>700°C SQR >700°C T2 >700C TF

49 50 NG 490 15 451 956

49 50 NG 185 18 510 713

49 50 NG 2100 26 1408 3534

133

49 50 NG 3010 34 1946 4990

49 50 NG 3586 38 2280 5904

49 50 NG 51 0 65 116

49 50 NG 36 3 110 149

49 50 NG 92 4 120 216 115

18 90 >700°C

D2,T3 N H2, C2H4 T3 NI NI

50 16 132/33 2 0 8 10 18 3 158 T OH T3 LTO NF

51 10 33/11 3 0 10 13 4

2 186 T N T3 NI NF

52 10 33/11 2 0 5 7 4 6 173 T N T3 NF NF

53 16 132/33 4 2 63 69 3.5

16 51 T OH T3 >700C NF

54 16 132/33 4 0 9 13 11 189 7 T OH T3 NF NI

55 6.3 33/11 3 0 17 20 4

6 25 T OH T3 LTO NF

56 6.3 33/11 2 0 8 10 4 4 144 T N T3 LTO NF

57 16 132/33 45 147 110 302 41

11 297 T AD D2 NI NI

58 16 132/33 52 214 106 372 103 7 275 T AD D2 He Arc DA

59 16 132/33 1 0 18 19 29

25 231 T OH T3 PD NF

60 16/20 132/33 17 0 17 34 60 10 405 T OH T3 LTO NF

61 16/20 132/33 1 0 12 13 1

16 262 T N T3 NF NF

62 16 132/33 3 42 64 109 2 31 290 OH OH DT NI NF

63 6.3 33/11 2 0 28 30 2

20 497 OH OH T3 LTO NF

64 20 66/11 47 196 72 315 4 Cir. C& AD C2H4,C2H2 DT NI NF

65 20 66/11 51 201 79 331 41

Cir. C& AD C2H4,C2H2 D2 NF NF

66 20 66/11 45 189 70 304 Cir. C& AD C2H4,C2H2 D2 NF NF

67 20 66/11 49 199 75 323

Cir. C& AD C2H4,C2H2 D2 NF NF

134

5.9 SUMMARY

In this chapter graphical and simulations of different Duval Triangle methods for DGA

provides the following results:

1. Duval Triangle Methods (DTMs) for DGA fault interpretations in power transformers

is very simple (with three gases only) and consuming less time.

2. These methods have satisfied the fault diagnosis (both graphical (manual) and

simulation implementation) more than 95% accurate than any other method of

diagnostics.

3. Traces of one of the three gases can provide the quick fault diagnosis to a little

experienced worker on the power transformers.

4. These methods always provide a diagnosis, with a very low percentage of wrong

diagnosis.

5. Duval triangle representation also allows to follow graphically in a very easy way and

the evaluation of faults with time visually.

6. From all above analysis and their developed simulations for all Duval Triangle

method 1 to Duval Triangle method5 respectively has proved that this technique can

provide full solutions in fault analysis of power transformers by DGA.

135

CHAPTER 6

APPLICATION OF ARTIFICIAL INTELLIGENCE IN DUVAL TRIANGLE

METHODS

6.1 INTRODUCTION

Certain amount of uncertainty in the data concerning dissolved gas analysis due to

generation, sampling, and chromatography analysis exist and thus variation in gas

interpretation affects the variation of the gases by the utilities. Due to the diverse gas

content of the insulating oil of transformers, many artificial intelligence (AI) techniques

may be used such as expert systems, support vector machines (SVM), fuzzy logic (FL),

artificial neural networks (ANN), genetic algorithm (GA) and even novel cerebellar

model articulation controller based method for off-line and on-line monitoring.

6.2 ARTIFICIAL INTELLIGENCE TECHNIQUES

Artificial intelligence (AI) is the intelligence of the machines and the branch of computer

science that aims to create it. AI can be said as the study of intelligent agent, where an

intelligent agent is a system that perceives its environment and takes action that

maximizes its chances of success. This was founded on claim that a central property of

human, intelligence can be precisely described that it can be simulated by a machine.

Therefore, artificial intelligence is a science based upon the human cognitive process. AI

has been the subject optimism, but also has suffered the setbacks. Nevertheless, today,

AI has become the part of the technology industry, providing the solutions to most

difficult problems in computer science.

6.3 FUZZY LOGIC TECHNIQUE (FLT)

Fuzzy logic is the theory of fuzzy sets, sets that calibrates roughness. Fuzzy is not logic

that is fuzzy, but a logic that is used to describe fuzzyness. Fuzzy logic is based on the

idea that all things admit of degrees, such as temperature, height, speed, beauty, distance,

etc, which can be seen of sliding scale. Thus fuzzy logic is a branch of fuzzy set theory,

which deals (similar to logic systems do) with the representation and inference form of

knowledge. The fuzzy analysis consists of three parts: fuzzification, fuzzy inference and

defuzzification. Fuzzification is the process of transforming crisp input values into grades

of membership for linguistic terms of fuzzy sets. The membership function is used to

associate a grade to each linguistic term. A chosen fuzzy inference system (FIS) is

responsible for drawing conclusions from the knowledge-based fuzzy rule set of if-then

linguistic statements. Fault types are first listed and then form the fuzzy rule set for the

136

diagnosis system. Defuzzification then converts the fuzzy output values back into crisp

output actions see Fig. 6.1.

Figure 6.1 Basic fuzzy logic control

A fuzzy set F of a universe of discourse X = 0, 1 is defined as a mapping, µF (x) : X →

[0, α], by which each x is assigned a number in the range [0, α], indicating the extent to

which x has the attribute F. If x is say ‗small‘ a value of fuzzy variables from the range

of ‗0 to ∞‘, then µsmall (x) € [0, α] is called a ‗membership function‘. A membership

function is normalized (ie., α = 1), thus µF (x) : X → [0, 1], fuzzy logic is called ‗normal‘.

The normalization of fuzzy set is expressed by

sup µ (x) = 1 (6.1)

x € X

Normalization of a set of numbers is achieved by dividing each number of the set by the

largest one, the supermum. Where, X be a time-invariant set of objects x. A fuzzy set is

X will be expressed by a set of ordered pairs:

= [(x, µ (x))| x € X ] (6.2)

Where, µ is the membership function that maps X to the membership space M (ie. 0 to

1) and µ (x) is the grade of membership (degree of truth) x in .

Tomsovic et al made use of fuzzy information approach to integrating different

transformer DGA diagnostic methods [51], which has shown that fussy relation are

combined with a faults tree to provide the best analysis. In this DGA has been

implemented with satisfying results. It could also be desirable to suggest further

measurements which could help clarify the diagnosis in marginal cases. Also in this paper

current implementations of the non-DGA methods were tried since these techniques are

gaining the experiences. Huang et al designed robust transformer DGA fault diagnosis

system with the help of evolutionary FL [52, 53]. Using IEC/IEEE criteria as reference,

an evolutionary based fuzzy system was developed to identify the incipient faults in the

Knowledge

Base

Fuzzyfication

Interface

Defuzzification

Interface

Decision

Making Logic

137

transformers. In this research the conventional DGA methods were improved in

accordance with the record of the practical examination of suspected transformers, an

evolutionary based optimization algorithm was employed to further modify the fuzzy if-

then rules and simultaneous adjust the membership function of fuzzy subsets. Yang et al

[54, 55] used adaptive fuzzy diagnostic system to enhance the fault diagnosis capabilities

for DGA of power transformers. Using practical gas records and associated fault causes, a

fuzzy reasoning algorithm was presented to establish a preliminary fuzzy diagnosis

system. The proposed scheme has enhanced the tuning of the fuzzy membership functions

a well as rule-pruning scheme for more compact diagnosis system. Zhang et al applied

fuzzy equivalent matrix (FEM) for fault diagnosis of oil-immersed insulation [56].

Compared with conventional methods, it is not based on inferring rules, but abstracting

fault information from the original data unsupervised to the recognition of DGA data for

power transformers. Subsequently, [57] which applied fuzzy data processing for DGA of

power transformers, fuzzy clustering analysis (FCA) technique introduced by the same

researcher to identify the fault patterns, its clustering centers are dynamic, which can

classify and recombine different samples successfully. Denghua et al [58] used a new

fuzzy information optimization processing for monitoring the transformers, used different

diagnostic methods and information as mathematics theory to establish the membership

functions of different data for oil-filled transformers. Q. Su [59, 60] utilized fuzzy logic

tool for transformer fault diagnosis. Since conventional interpretation methods of DGA

such as IEC codes can detect faults in many cases, where more than one fault exists in a

transformer. Thus, this technique is very useful tool for diagnosis and maintenance

planning of power transformers.

Hooshmand et al [61] have applied fuzzy systems in DGA fault diagnosis using IEC,

ASTM and CEGB standards and categorized the results into two groups: crisp and fuzzy

set. One of the problems in fault diagnosis of transformer based on dissolved gas is lack

of matching the result of fault diagnosis of different standards with the real world. The

result of the different standards is analyzed using fuzzy and the result is compared with

the empirical test. The comparison between the suggested method and existing methods

indicate the capability of the suggested method in on-line fault diagnosis of the

transformers. In addition, in some cases the existing standards are not able to diagnose the

fault. The presented method has the potential of diagnosing the fault. The used the

information of three transformers to the show the capability of the suggested method in

138

diagnosing the fault. The results validate the capability of the presented method in fault

diagnosis of the transformer.

Flores et al [62] applied type-2 fuzzy logic as novel algorithm in fault diagnosis of power

transformers through dissolved gas analysis. Afiqah et al. [63] proposed two DGA

methods which are Rogers Ratio and IEC Ratio to interpret the DGA results. However,

there are situations of errors and misleading results occurring due to borderline and

multiple faults. Fuzzy logic is implemented here as an improved DGA interpretation

method that provides higher reliability and precision of fault diagnosis. Aragon-Patil at al

[64] has shown improvements of interpretation of DGA in power transformers for IEC

code ratios. Sun et al [65] utilized in research with fuzzy model hierarchy classification in

his study for fault diagnosis of power transformers through DGA.

6.4 FUZZY LOGIC APPROACH IN DTM1

The fuzzy logic approach used for fault diagnosis of power transformer with inputs and

an output is depicted in Fig. 6.2.

Figure 6.2: Fuzzy logic approach in DTMs

The following steps are used in Duval triangle method for fault diagnosis of power

transformers:

Step 1; Firstly, polygon coordinates for the numerical zone boundaries of seven key

faults of Duval Triangle method 1 are used in terms of percentages of CH4, C2H4 and

C2H2, from 0% to 100% respectively as mentioned in Table 5.6.

Step 2; In this diagnostic each crisp value of gas percentages (CH4, C2H4, C2H2) from in

Table 5.6, which are changed to triangular fuzzy-membership function ranges (ie.

139

A.......M, A1......I1 and A2......H2 respectively) shown in Table 6.1. Fuzzy-membership

function for input gases obtained in MATLAB fuzzy-box tool is illustrated in Fig. 6.3(a)

to Fig. 6.3(c).

Table 6.1: Membership function ranges for three gas inputs of DTM1

No. CH4 C2H4 C2H2

1 A 0-30 A1 0-1 A2 0-1

2 B 25-34 B1 1-15 B2 1-3

3 C 34-35 C1 10-22 C2 3-10

4 D 35-46 D1 22-32 D2 8-14

5 E 46-49 E1 30-42 E2 14-25

6 F 49-60 F1 40-55 F2 25-40

7 G 55-75 G1 55-75 G2 70-85

8 H 70-79 H1 70-87 H2 80-100

9 I 79-86 I1 85-100

10 J 85-95

11 K 90-97

12 L 97-99

13 M 99-100

Figure 6.3(a): %CH4 membership-function

140

Figure 6.3(b): %C2H4 member functions

Figure 6.3(c): % C2H2 member functions

Step 3: Fuzzy outputs- transformer faults; In this diagnostic, each crisp value for

transformer faults are further rearranged, shown in Table 6.2 and changed to output

trapezium fuzzy-membership function into 33 faults. Fuzzy-membership function for

faults by DTM1 are obtained and illustrated in Fig. 6.4.

Table 6.2: Transformer fault outputs DTM 1

No. Area Points Faults

1 PD PD1 1

PD2 2

PD3 3

2 D1 D11 4

D12 5

D13 6

D14 7

141

3 D2 D21 8

D22 9

D23 10

D24 11

D25 12

4 DT DT1 13

DT2 14

DT3 15

DT4 16

DT5 17

DT6 18

DT7 19

DT8 20

5 T1 T11 21

T12 22

T13 23

T14 24

T15 25

6 T2 T21 26

T22 27

T23 28

T24 29

7 T3 T31 30

T32 31

T33 32

T34 33

142

Figure 6.4: Membership function of DTM 1 faults

Step 4; Fuzzy-rule base; The set of fuzzy inputs of three gases assigned with crisp

values (% three gases) with their respective membership function form the integral part of

fuzzy logic analysis. The fuzzy rule set (If-then linguistic statements) with AND operator

for minimum and OR operator for maximum fault conditions is then used form

‗judgment‘ on the fuzzy inputs derived from the three gas percentages, whose sum is

always 100%. For example;

All such thirty three fuzzy rules just derived for mapping the fault types and screenshot of

these rules is shown Table 6.3 in DTM1. Although these faults are defined strictly for the

percentages of the zone boundaries of seven faults in Duval triangle method 1

coordinates.

Table 6.3: Fuzzy Rules for DTM 1

Rules % Gas input Membership Function Output

CH4 C2H4 C2H2

1 L A1 B2 PD1

2 M A1 A2 PD2

3 L B1 A2 PD3

4 A A1 H2 D11

5 A D1 G2 D12

6 G D1 D2 D13

7 J A1 D2 D14

8 A D1 G2 D21

9 A H1 F2 D22

10 B E1 F2 D23

11 E E1 D2 D24

143

12 G D1 D2 D25

13 A G1 F2 DT1

14 A H1 E2 DT2

15 C F1 E2 DT3

16 D F1 C2 DT4

17 K A1 C2 DT5

18 J A1 D2 DT6

19 E E1 D2 DT7

20 B E1 F2 DT8

21 H C1 C2 T11

22 I C1 A2 T12

23 L B1 A2 T13

24 L A1 B2 T14

25 K A1 C2 T15

26 D F1 C2 T21

27 F F1 A2 T22

28 I C1 A2 T23

29 H C1 C2 T24

30 A H1 E2 T31

31 A H1 A2 T32

32 F F1 A2 T33

33 C F1 E2 T34

Step 5; Fuzzy Inference System (FIS), FIS involves the operation between input fuzzy

sets as shown in Fig. 6.5, known as ‗Mamdani‘ type. This derives output fuzzy sets

‗judging‘ all the possible fuzzy rules by finding the membership for the fault types as

represented by thirty three fuzzy output rules. The solution is reached by weighted

average of the fuzzy inputs. The spikes in the figure denoting probabilities between 0 and

1 are the outputs from each fuzzy rule, which denotes the fault type. Thus each rule is a

row of the plots and each column is variable.

144

Figure 6.5: FIS analysis of fuzzy rules for DTM1

6.5 FUZZY LOGIC APPROACH FOR DTM 2

Fuzzy logic approach for fault diagnosis in oil-filled OLTCs with the help of DTM2, the

similar steps are followed as the fault diagnosis by DTM1 for the incipient faults in the

power transformers is adopted. The approach as follows:

Input ranges: The ranges of three inputs gases in DTM2 for fault diagnosis in OLTCs of

power transformers are given in Table 6.4.

Table 6.4: DTM 2 ranges of input gases

CH4 C2H4 C2H2

A1 1-20 A2 6-22 A3 58-92

C1 20-100 C2 22-85 C3 78-100

D1 0-62 D2 22-50 D3 0-81

E1 35-76 E2 50-100 E3 15-80

F1 0-50

F3 0-15

The DTM2 triangular fuzzy-membership function ranges are arranged as (ie. A1.......H1,

A2......F2 and A3......H3 respectively) for the three input gas percentages of (CH4, C2H4,

C2H2). These are shown in Fig. 6.6 (a) – Fig. 6.6 (c) for corresponding three gases.

145

Figure 6.6 (a): DTM 2 Membership functions of CH4

Figure 6.6 (b) DTM 2 Membership functions of C2H4

Figure 6.6 (c): DTM 2 Membership functions of C2H2

Output of DTM 2; Ranges for OLTC‘s six fault outputs are given in Table 6.5 and their

fuzzy membership function for the same output ranges are shown in Fig. 6.7.

Table 6.5: Types of faults in OLTCs

No. Faults Ranges

1 N 0-10

146

2 D1 10-30

3 X1 20-40

4 X3 40-50

5 T2 50-70

6 T3 60-70

Figure 6.7: OLTC‘s faults output ranges

Fuzzy rules; Fuzzy rules for the fault diagnosis in oil filled OLTC tank for the given

input and output combinations are shown in Table 6.6.

Table 6.6: Fuzzy rules for OLTC faults


CH4 C2H4 C2H2

1 A1 A2 A3 N

2 A1 B2 B3 D1

3 C1 B2 D3 X1

4 D1 C2 E3 X3

5 E1 D2 F3 T2

6 F1 F2 F3 T3

Fuzzy fault output in FIS analysis in DTM2 for given rules are shown in Fig. 6.8.

147


6.6 FUZZY LOGIC APPROACH FOR DTM 3

For fuzzy logic approach for DTM3 in fault diagnosis for synthetic oils will involve the

same procedure like the steps followed for fault diagnosis for DTM1 and DTM2. There

are four types of synthetic oil (Silicone, Midel, FR3 and BioTemp oil) are available with

different fault zones, and number of faults. Therefore, separate four DTM3 are used for

four types of synthetic oils. These are as follows:

6.6.1 Fuzzy Logic Approach for DTM3, Silicone oil

Input ranges: Inputs to the fuzzy logic DTM3 are arranged in the different ranges and

shown in Table 6.7. The fuzzy membership functions of same three input gases are shown

in Fig. 6.9 (a) to Fig. 6.9 (c).

Table 6.7: Input Ranges for DTM3, Silicone oil

CH4 C2H4 C2H2

A1 0-25 A2 0-1 A3 0-1

B1 25-35 B2 1-3 B3 1-3

C1 35-45 C2 5-15 C3 3-7

D1 45-49 D2 15-25 D3 8-13

E1 48-50 E2 35-45 E3 12-28

F1 50-60 F2 45-55 F3 20-30

G1 70-80 G2 70-80 G3 80-95

H1 75-80 H2 80-90 H3 95-100

I1 80-88 I2 90-100

J1 85-90

148

K1 90-97

L1 97-99

M1 99-100

Figure 6.9(a): Membership functions for CH4 DTM3, Silicone oil

Figure 6.9(b): Membership functions for C2H4 DTM3, Silicone oil

Figure 6.9(c): Membership function for C2H2 DTM3, Silicone oil

149

DTM3 faults in Silicone oil: Output membership function for DTM3 Silicone oil are

further subdivided, shown in Table 6.8 and fuzzy membership functions from fuzzy tool-

box are shown in Fig. 6.10

Table 6.8: Fault output membership functions of DTM3, Silicone oil


1 PD PD1 1

PD2 2

PD3 3

2 D1 D11 4

D12 5

D13 6

D14 7

3 D2 D21 8

D22 9

D23 10

D24 11

D25 12

4 DT DT1 13

DT2 14

DT3 15

DT4 16

DT5 17

DT6 18

DT7 19

DT8 20

5 T1 T11 21

T12 22

T13 23

T14 24

T15 25

6 T2 T21 26

150

T22 27

T23 28

T24 29

7 T3 T31 30

T32 31

T33 32

T34 33

Figure 6.10: Output membership functions of silicone oil

Fuzzy rules; Fuzzy rules for fault diagnosis through DTM3, silicone oil for the above

mentioned inputs and output membership functions are stated in Table 6.9.

Table 6.9: Fuzzy Rules for DTM3, Silicone oil


CH4 C2H4 C2H2

1 L1 B2 A3 PD1

2 M1 A2 A3 PD2

3 L1 B2 A3 PD3

4 A1 A2 H3 D11

5 J1 A2 D3 D12

6 A1 C2 G3 D13

7 A1 C2 G3 D14

8 G1 C2 D3 D21

151

9 B1 E2 F3 D22

10 D1 E2 D3 D23

11 G1 C2 D3 D24

12 A1 G2 F3 D25

13 A1 G2 F3 DT1

14 B1 E2 F3 DT2

15 D1 E2 D3 DT3

16 J1 A2 B3 DT4

17 K1 A2 C3 DT5

18 E1 A2 C3 DT6

19 C1 F2 E3 DT7

20 A1 H2 E3 DT8

21 K1 A2 C3 T11

22 L1 A2 B3 T12

23 L1 B2 A3 T13

24 I1 D2 A3 T14

25 H1 B2 C3 T15

26 I1 F2 A3 T21

27 H1 D2 A3 T22

28 E1 F2 C3 T23

29 F1 F2 A3 T24

30 F1 F2 A3 T31

31 C1 F2 E3 T32

32 A1 H2 E3 T33

33 A1 I2 A3 T34

Fuzzy fault output in FIS analysis in DTM3, silicone oil for given rules are shown in Fig.

6.11.

152

Figure 6.11: FIS analysis of fuzzy rules for DTM3, Silicone oil

6.6.2 Fuzzy Logic Approach for DTM3, Midel Oil

Input ranges; The ranges of input for DTM3 Midel oil and their fuzzy membership are

given in Table 6.10 and Fig. 6.12 (a) – 6.12 (c) for three input gases in fuzzy logic

approach.

Table 6.10: Ranges of input for DTM3, Midel oil

CH4 C2H4 C2H2

A 0-10 A 0-1 A 0-1

B 10-20 B 1-5 B 1-3

C 20-30 C 20-30 C 3-8

D 28-32 D 35-45 D 5-15

E 30-35 E 65-70 E 12-15

F 40-48 F 70-75 F 20-30

G 50-60 G 80-90 G 65-75

H 58-63 H 90-100 H 90-100

I 80-90

J 90-95

K 95-98

L 98-100

153

Figure 6.12 (a): Fuzzy membership function of CH4 for DTM3, Midel oil

Figure 6.12 (b): Fuzzy membership function of C2H4 for DTM3, Midel oil

Figure 6.12 (c): Fuzzy membership function of C2H2 for DTM3 Midel oil

DTM3 fault output; There are thirty three fault outputs for DTM3, Midel oil shows in

Table 6.11, and fuzzy membership function listed in Fig. 6.13.

154

Table 6.11: Output ranges for DTM3, Midel oil

No. Area Points faults

1 PD PD1 1

PD2 2

PD3 3

2 D1 D11 4

D12 5

D13 6

D14 7

3 D2 D21 8

D22 9

D23 10

D24 11

D25 12

4 DT DT1 13

DT2 14

DT3 15

DT4 16

DT5 17

DT6 18

DT7 19

DT8 20

5 T1 T11 21

T12 22

T13 23

T14 24

T15 25

6 T2 T21 26

T22 27

T23 28

T24 29

155

7 T3 T31 30

T32 31

T33 32

T34 33

Figure 6.13: Output membership functions for DTM3, Midel oil

Fuzzy rules; There are thirty three output fuzzy rules are framed for DTM3, Midel oil

shown in Table 6.11.

Table 6.12: Fuzzy rules for DTM3, Midel oil


CH4 C2H4 C2H2

1 K B A PD1

2 L A A PD2

3 K B A PD3

4 A A H D11

5 I A D D12

6 H C D D13

7 A C G D14

8 A C G D21

9 H C D D22

10 F D D D23

11 E D F D24

12 A E F D25

156

13 I A C DT1

14 K A C DT2

15 C E C DT3

16 B E E DT4

17 A G E DT5

18 A E F DT6

19 E D F DT7

20 A G E DT8

21 J A C T11

22 L A B T12

23 L B A T13

24 H D A T14

25 G D C T15

26 G D C T21

27 H D A T22

28 D E A T23

29 C E E T24

30 D E A T31

31 B E E T32

32 A G E T33

33 A H A T34

Fuzzy fault output in FIS analysis in DTM3, midel oil for given rules are shown in Fig.

6.14.

157

Figure 6.14: FIS analysis of fuzzy rules for DTM3, Midel oil

6.6.3 Fuzzy Logic Approach for DTM3, FR3 Oil

Fuzzy logic approach for DTM3, FR3 oil in DGA fault diagnosis of power transformers

as follows:

Input ranges; Three input ranges for DTM3, FR3 oil are shown in Table 6.13.

Table 6.13: Input ranges for DTM3, FR3 oil

CH4 C2H4 C2H2

A 0-10 A 0-1 A 0-1

B 10-20 B 0-20 B 0-3

C 20-30 C 20-30 C 3-10

D 25-35 D 30-40 D 7-15

E 30-45 E 40-45 E 15-20

F 40-48 F 45-50 F 25-35

G 45-55 G 55-65 G 70-80

H 50-55 H 65-75 H 90-100

I 55-60 I 80-90

J 60-70 J 90-100

K 80-90

L 85-95

M 95-100

N 98-100

158

Fuzzy membership functions for above stated inputs of DTM3 FR3 oil are shown in the

Fig.6.15 (a) – Fig. 6. 15 (c).

Figure 6.15 (a): Fuzzy membership functions for DTM3, FR3 oil

Figure 6.15 (b): Fuzzy membership functions for DTM3, FR3 oil

Figure 6.15 (c): Fuzzy membership functions for DTM3, FR3 oil

159

Fault outputs; Thirty three outputs are assigned for fault diagnosis of power transformer

by DTM3, FR3 oil are given in Table 6.14.

Table 6.14: Fault output for DTM3, FR3 oil


1 PD PD1 1

PD2 2

PD3 3

2 D1 D11 4

D12 5

D13 6

D14 7

3 D2 D21 8

D22 9

D23 10

D24 11

D25 12

4 DT DT1 13

DT2 14

DT3 15

DT4 16

DT5 17

DT6 18

DT7 19

DT8 20

5 T1 T11 21

T12 22

T13 23

T14 24

T15 25

6 T2 T21 26

T22 27

160

T23 28

T24 29

7 T3 T31 30

T32 31

T33 32

T34 33

Output membership functions for DTM3, FR3 oil in fuzzy environment are given in Fig.

6.16.

Figure 6.16: Output membership functions for DTM3 FR3 oil

Fuzzy rules; Fuzzy rules for DTM3, FR3 oil fault diagnosis approach are shown in Table

6.15.

Table 6.15: Fuzzy rules for DTM3, FR3 oil


CH4 C2H4 C2H2

1 L1 B2 A3 PD1

2 M1 A2 A3 PD2

3 L1 B2 A3 PD3

4 A1 A2 H3 D11

5 J1 A2 D3 D12

6 A1 C2 G3 D13

7 A1 C2 G3 D14

161

8 G1 C2 D3 D21

9 B1 E2 F3 D22

10 D1 E2 D3 D23

11 G1 C2 D3 D24

12 A1 G2 F3 D25

13 A1 G2 F3 DT1

14 B1 E2 F3 DT2

15 D1 E2 D3 DT3

16 J1 A2 B3 DT4

17 A1 I2 E3 DT5

18 A1 H2 F3 DT6

19 F1 D2 F3 DT7

20 F1 D2 D3 DT8

21 L1 A2 C3 T11

22 N1 A2 B3 T12

23 N1 B2 A3 T13

24 I1 E2 A3 T14

25 H1 E2 C3 T15

26 H1 F2 C3 T21

27 I1 E2 A3 T22

28 E1 G2 A3 T23

29 D1 G2 C3 T24

30 E1 G2 A3 T31

31 C1 G2 E3 T32

32 A1 I2 E3 T33

33 A1 J2 A3 T34

Fuzzy fault output in FIS analysis in DTM3, FR3 oil for given rules are shown in Fig.

6.17.

162

Figure 6.17: FIS analysis of fuzzy rules for DTM3, FR3 oil

6.6.4 Fuzzy Logic Approach for DTM 3, Bio-Temp Oil

Similar way, a fuzzy logic approach is also applied for DTM3, BioTemp oil in DGA fault

diagnosis of power transformers as:

Input ranges; Three gas inputs are arranged in different ranges for DTM3, BioTemp oil

filled transformers for fault diagnosis given in Table 6.16.

Table 6.16: Input ranges for DTM3, BioTemp oil

CH4 C2H4 C2H2

A 0-10 A 0-1 A 0-1

B 4-15 B 1-3 B 1-3

C 15-25 C 10-20 C 2-5

D 25-35 D 20-30 D 5-15

E 35-45 E 34-45 E 10-20

F 45-50 F 50-60 F 25-35

G 48-56 G 70-80 G 70-80

H 60-70 H 75-85 H 80-90

I 80-90 I 85-95 I 90-100

J 90-97 J 90-100

K 99-100

163

Triangular fuzzy membership function ranges for three gas inputs of DTM3, BioTemp oil

are shown in Fig. 6.18 (a) – Fig. 6.18 (c).

Figure 6.18 (a): Input membership function CH4 in DTM3, BioTemp oil

Figure 6.18 (b): Input membership function C2H4 in DTM3, Biotemp oil

Figure 6.18 (c): Input membership function of C2H2 in DTM3, BioTemp oil

Fault outputs; In DTM3, BioTemp oil outputs assigned for fault diagnosis of power

transformer are given in Table 6.17.

Table 6.17: Fault output for DTM3 BioTemp

No. Area Points faults

1 PD PD1 1

164

PD2 2

PD3 3

2 D1 D11 4

D12 5

D13 6

D14 7

3 D2 D21 8

D22 9

D23 10

D24 11

D25 12

4 DT DT1 13

DT2 14

DT3 15

DT4 16

DT5 17

DT6 18

5 T1 T11 19

T12 20

T13 21

T14 22

T15 23

6 T2 T21 24

T22 25

T23 26

T24 27

7 T3 T31 28

T32 29

T33 30

Fuzzy membership functions for fault output variables from DTM3, BioTemp oil are

shown in Fig. 6.19.

165

Figure 6.19: Fuzzy outputs of DTM3, BioTemp oil

Fuzzy rules; Fuzzy rules for assigned thirty outputs in DTM3, BioTemp oil fault

diagnosis of power transformer are given in Table 6.18.

Table 6.18: Fuzzy rules for DTM3, BioTemp oil


CH4 C2H4 C2H2

1 K A B PD1

2 K A A PD2

3 K B A PD3

4 A A I D11

5 I A D D12

6 H D D D13

7 A D G D14

8 A D G D21

9 H D D D22

10 G E D D23

11 D E F D24

12 A G F D25

13 I A D DT1

14 J A C DT2

15 B H C DT3

16 B I D DT4

17 D E F DT5

18 G E D DT6

166

19 J A C T11

20 K A B T12

21 K B A T13

22 F F A T14

23 E F C T15

24 E F C T21

25 F F A T22

26 C H A T23

27 B H C T24

28 A H E T31

29 A C H T32

30 A J A T33

Fuzzy fault output in FIS analysis in DTM3, BioTemp oil for given rules are shown in

Fig. 6.20.

Figure 6.20: FIS analysis of fuzzy rules for DTM3, BioTemp oil

167

6.7 FUZZY LIGIC APPROACH FOR DTM4, LOW ENERGY FAULTS

DTM4 is used for low intensity faults over and above the fault diagnosis with DTM1. The

procedure is similar to fault diagnosis to other DTMs. This follows as:

Input ranges; DTM4 is used low energy faults in the main transformer tank, input gas

ranges (H2, CH4, C2H6) are given in Table 6.19.

Table 6.19: Input ranges for DTM4

H2 CH4 C2H6

A 0-5 A1 0-1 A2 0-0.5

B 5-15 B1 1-3 B2 0.5-5

C 30-45 C1 10-20 C2 20-30

D 40-55 D1 20-30 D2 40-55

E 54-62 E1 30-40 E2 75-88

F 62-72 F1 55-68 F2 90-100

G 72-82 G1 68-75

H 82-86 H1 75-85

I 84-88 I1 90-100

J 90-95

K 90-98

L 97-100

Fuzzy membership functions for inputs in DTM4 used for low energy faults occurring in

power transformers are shown in Fig. 6.21(a) – Fig. 6.21(c).

Figure 6.21 (a): Fuzzy membership functions of H2 for DTM4

168

Figure 6.21 (b): Fuzzy membership functions of CH4for DTM4

Figure 6.21 (c): Fuzzy membership functions of C2H6 for DTM4

Fault outputs; Faults output ranges for DTM4 are described and shown in Table 6.20.

Table 6.20: DTM4 output ranges


1 PD PD1 1

PD2 2

PD3 3

PD4 4

2 O O1 5

O2 6

O3 7

O4 8

3 S S1 9

S2 10

169

S3 11

S4 12

S5 13

S6 14

S7 15

S9 16

S10 17

4 C C1 18

C2 19

C3 20

C4 21

Fuzzy rules; Fuzzy rules for the input and output ranges for DTM 4 for fault diagnosis

are given in Table 6.21.

Table 6.21: Fuzzy rules for DTM4


H2 CH4 C2H6

1 L B1 A2 PD1

2 I C1 A2 PD2

3 H C1 B2 PD3

4 J B1 B2 PD4

5 E A1 D2 O1

6 L A1 A2 O2

7 L B1 A2 O3

8 K B1 B2 O4

9 H C1 B2 S1

10 F E1 A2 S2

11 G D1 A2 S3

12 D E1 C2 S4

13 B F1 C2 S5

14 A F1 C2 S6

15 C E1 C2 S7

170

16 D E1 A2 S8

17 B F1 A2 S9

18 A A1 F2 C1

19 B A1 E2 C2

20 B G1 C2 C3



6.8 FUZZY LIGIC APPROACH FOR DTM5, LOW TEMPERATURE FAULTS

DTM5 is used as low -temperature faults in the main transformer tank. This fault

diagnosis method is used along with DTM1 to ascertain fault in low intensity electrical

discharges in transformers. Rest of the procedure is similar to other DTMs as follows:

Input ranges; Ranges of three inputs for DTM5 used for fault in power transformers are

arranged as given in Table 6.22.

Table 6.22: Input ranges of DTM5

CH4 C2H4 C2H6

A1 0-5 A2 0-1 A3 0-0.5

B1 5-15 B2 1-3 B3 0.5-5

171

C1 30-45 C2 10-20 C3 20-30

D1 45-55 D2 20-30 D3 40-55

E1 54-62 E2 30-40 E3 75-88

F1 62-72 F2 55-68 F3 90-100

G1 72-82 G2 68-75

H1 82-86 H2 75-85

I1 84-88 I2 90-100

J1 90-95

K1 90-98

L1 97-100

M1 98-100

Fuzzy membership functions for the three inputs of DTM5 are shown in Fig. 6.23 (a) –

Fig. 6.23 (c).

Figure 6.23 (a): Fuzzy membership functions of CH4 in DTM5

Figure 6.23 (b): Fuzzy membership functions of C2H4 in DTM5

172

Figure 6.23 (c): Fuzzy membership functions of C2H6 in DTM5

Fault outputs; Fault outputs in DTM5 are rearranged to represent faults are given in

Table 6.23.

Table 6.23: Fault outputs in DTM5


1 PD PD1 1

PD2 2

PD3 3

PD4 4

2 S S1 5

S2 6

S3 7

S4 8

3 O O1 9

O2 10

O3 11

O4 12

O5 13

O6 14

O7 15

O9 16

O10 17

O11 18

173

4 C C1 19

C2 20

C3 21

C4 22

5 T3 T31 23

T32 24

T33 25

DTM5 outputs as fuzzy membership functions are given in Fig. 6.24.

Figure 6.24: Fuzzy output membership function for DTM5

Fuzzy rules; Fuzzy rules in DTM5 for fault diagnosis for input output combinations are

given in Table 6. 24.

Table 6.24: Fuzzy rules for DTM5


CH4 C2H4 C2H2

1 A1 A2 C3 PD1

2 I1 A2 B3 PD2

3 K1 B2 B3 PD3

4 H1 D2 B3 PD4

5 D1 A2 E3 S1

6 I1 A2 C3 S2

7 G1 C2 CE S3

8 C1 C2 E3 S4

174

9 A1 A2 H3 O1

10 D1 A2 E3 O2

11 C1 C2 E3 O3

12 A1 C2 G3 O4

13 I1 A2 C3 O5

14 H1 D2 B3 O6

15 G1 C2 C3 O7

16 I1 C2 A3 O8

17 L1 A2 A3 O9

18 L1 A2 B3 O10

19 K1 B2 B3 O11

20 E1 C2 D3 C1

21 B1 E2 D3 C2

22 F1 E2 A3 C3

23 J1 C2 A3 C4

24 A1 F2 F3 T31

25 F1 E2 A3 T33

26 A1 G2 A3 T32



175

6.9 ARTIFICIAL NEURAL NETWORK TECHNIQUES (ANNT)

Artificial Neural Networks is a massively parallel distributed processor as shown in Fig.

6.26, having a natural tendency to acquire sufficient experimental Knowledge and making

it available for use.

Figure 6.26: Two-layered Feedforward Nueral Network

A neuron model is the most basic information processing unit in a neural network.

Depending on complexity they are organized in three or more layers: the input layer, the

output layer and one or many hidden layers. Each neuron model receives input signals,

which are multiplied by synaptic weights. An activation function transforms these signals

into an output signal to the next neuron model and so on. Generalization ability and

learning stability are the two main issues for multilayer feed-forward neural network

(FNN). A back-propagation algorithm is a well suitable method for training the multilayer

feed-forward ANN, which has good ability of pattern recognition. Hencefore , a well

suitable Feed-forward Back-propagation Two-layer artificial neural network has been

applied in Duval Triangle 1 method of fault diagnosis in power transformers in this study.

A sigmoid activation transfer function is applied because of its two advantages: firstly, it

is highly nonlinear, and secondly, it has good performance when working with back-

propagation learning algorithms. Typically, for the conventional back-propagation (BP)

training algorithm , the mean square error (MSE) is used. The mean square error is

defined as follows:

(6.3)

where, Ep is the error for the pth pattern vector, ypj is the desired value of the output node

and opj is the actual output of the jth output node. The aim of learning is to minimize the

instantaneous square error of the output signal by modifying the weights and bias the bias

176

term can be regarded as weights and iteratively alters in the same manner as the other

weights. The ‗sigmoid‘ function used is as follows:

Opj = f(netpj) = 1/ (1 + e-netpj -qj

) (6.4)

Here,

netpj (6.5)

where, the sum is taken over the output Opj, if all nodes in layer ( L-1).

Among these AI techniques, the ANN is widely designed to diagnose transformer faults.

An important advantage of fault diagnosis based on the ANN is that it can learn directly

from the training samples and update its knowledge when necessary. The high nonlinear

mapping capability of neurons always provides a comparable and superior performance

over a fuzzy system solution. However, although the computational complexity of the

ANN is not too high, especially in the fault diagnostic process, it involves certain

problems including slow convergence, oscillation and so on, all of which must be

resolved before it is practically applied.

An Artificial Neural Network included selection of inputs, outputs, network topology and

weighted connection of node. Input feature-selection constitutes an essential first step.

This is chosen very carefully so that the input features will correctly reflects the

characteristics of the problem. Another major task of the ANN design is to choose

network topology. This is done experimentally through a repeated process to optimize the

number of hidden layers and nodes according to training performance and prediction

accuracy.

For many years there was no theoretically sound algorithm for training multi-layer ANN,

and therefore, the applications of ANN were severely limited. The invention of Back-

propagation algorithm has played a vital role in the resurgence of interest in ANN. Back-

propagation is a systematic method for training multi-layer ANN. It has a strong

mathematical foundation.

A two-layer feed-forward network is shown in Fig. 6.25. It consists of number of neurons

connected by links divided into two layers. A set of inputs is applied from outside or from

previous layer. Each of these is multiplied by a corresponding weight w. The sum of the

weighted inputs and the bias .b. forms the input .n. to the transfer/activation function ―F‖.

Neurons may use any differentiable, monotonic increasing transfer functions to generate

their outputs. Back-propagation networks often use the log-sigmoid and tan-sigmoid

transfer functions.

177

Zhang et al [66] proposed ANN approach to transformer fault diagnosis with DGA and

paper insulation degradation. In the current study, five key gases H2, CH4, C2H6, C2H4,

and C2H2 are chosen as input features. Since overheating, partial discharge and arcing are

the three major fault types in power transformers; hence there will be four output patterns

to be identified including the normal condition. A two step ANN method is used to detect

fault with and without paper cellulose, and obtained the good accuracy. Y. M. Tu et al

[67] utilizes improved ANN for many methods and criteria of DGA diagnosis to produce

the comparative study. The ANN will extract essential characteristics from the input data

without physical model to improve the accuracy of diagnostic. The diagnosing accuracy

of ANN will be improved further by using multi-subdivided ANNs and biological nerves.

Yanming et al [68] applied BP-ANN in the collected DGA of seven gases data from

transformer faults with improved algorithm is used in which the adjustment of the

weights is adaptive. Zheng et al [69] tested a synthetic diagnostic method for faults of oil

immersed power transformers and suggested many of its advantages. Thang et al [70] a

comparison of conventional DGA interpretations methods such CIGRE‘s, IEC, Roger‘s,

Dornenburg‘s ratios are discussed. Finally, a new approach includes simple statistical

analysis on DGA record and followed by high level data-mining using self – organizing

map algorithm to prove alternate for incipient fault diagnosis and monitoring applications.

Guardalo et al [71] presented a comparative study of neural network efficiency for

detection of incipient faults in transformers. ANN was trained for five DGA diagnostic

methods ie Roger‘s, Modified Roger,s, Dornenburg,s, IEC and CSUS, the study shown

that NN rate of successful fault evaluation is in the range of 87-100%. Patel et al [72]

introduces ANN based transformer fault diagnosis to show the application of adaptive BP

learning algorithm, which drastically reduces the number of epochs required to increase

the accuracy of the diagnosis compared to conventional BP algorithm. Dhlamini et al

presented parallel ANN [73] to successfully diagnose the condition of bushing through

DGA as per IEEE C-57-104 along with other methods and revealed that ANN produces

the accuracy of 99%.

Yan-jing et al [74] improved the back propagation (BP)-based artificial neural networks

(ANN) can identify complicated relationships among dissolved gas contents in

transformer oil and corresponding fault types, using the highly nonlinear mapping nature

of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and

Momentum coefficient) algorithm is proposed to reduce the training time and avoid being

trapped into local minima, where the learning rate and the momentum coefficient are

178

altered at iterations. We developed a system of transformer fault diagnosis based on

Dissolved Gases Analysis (DGA) with a BP-ALM algorithm. Training patterns were

selected from the results of a Refined Three-Ratio method (RTR). Test results show that

the system has a better ability of quick learning and global convergence than other

methods and a superior performance in fault diagnosis compared to convectional BP-

based neural networks and RTR.

Moreira et al [75] developed power transformer diagnosis system on two neural networks.

This uses gaseous chromatography and physical-chemical analysis. Results obtained

provided high accuracy rate of correct diagnosis compared with commonly used

traditional methods in addition proposed system presented itself as an alternative to

automation of the analysis process. Xiang-Hao at al [76] proposed artificial immune

network (ANIC) algorithm mimics the adaptive learning and defense mechanism to

respond to fault sample of the transformers, which has shown remarkable diagnosis

accuracy, multiple incipient faults can be classified effectively.

6.10 ANN APPROACH FOR DTM 1

Following steps are used in DTM 1 for fault diagnosis of power transformers with

artificial neural network technique:

Step 1; Polygon coordinates for the numerical zone boundaries of seven key faults of

Duval Triangle 1 are already stated in Table 5.6 and the same input percentages of

numerical zones are being used to create strings of three gas ratios (CH4, C2H4 and C2H2).

Step 2; Input and output patterns A complete ANN includes selection of inputs,

outputs, network topology and weighted connections of nodes. In this thesis the input

pattern are obtained from the DGA fault diagnosis reports of the faulty transformers

collected from organizations/utilities across INDIA. For each input pattern there exists an

output pattern which describes the fault types for a given diagnosis criterion. Both input

and output pattern formulate an ANN training set. Since Duval triangle 1 needs only three

combustible gases methane (CH4), ethylene (C2H4) and acetylene (C2H2), therefore, for

this study, input and output patterns for fault diagnosis in power transformers are as

follows:

[Input] = [x] = [%CH4 %C2H4 %C2H2] T

[0utput] = [O] = [PD D1 D2 DT T1 T2 T3] T

Output represents faults and the number assigned for each is as follows:

[O] = [1 2 3 4 5 6 7] T

179

A brief parameter details for this thesis work of DGA fault diagnosis in power

transformers through ANN based Duval triangle method 1 approach are shown in Table

6.25.

Table 6.25: Parameters for ANN Duval Triangle method 1

Input [%CH4 %C2H6 %C2H2]T

Output Faults [1 2 3 4 5 6 7]T

Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%

Number of Samples 1236

Epochs 53

Training Function Levenberg-Marquardt

(trainlm) Performance

Functions

Mean Square Errors (MSE)

Data Division Random (dividerand)

Network Types Feed-forward Back-

propagation Number of Neurons 2

Properties of Layer 1

Number of Neurons 10

Transfer Function TANSIG

Properties of layer 2

Transfer Function PURELIN

Training Parameters As desired

Epochs 1000

Time Infinite

Goal 0

Step 3; Neural Network configurations and training For this thesis two-layer feed-

forward network used. It is consist of number of neurons connected by links divided into

two layers. ANN training is the process by which synoptic weights in the network are

adjusted according to signal received. These signals are the input and output vectors

defined previously. Here BP network with tan-sigmoid (TANSIG) transfer function has

been used. Also ANN training purposes Levenberg-Marquardt (trainlm) is used. A

complete configuration and training of two-layer ANN in MATLAB environment is

shown in Fig. 6.27.

180

Figure 6.27: Two-layer BP-ANN for DTM1

Figure 6.26 shows that for training purposes of ANN, there are three fault gases as input

to the BP network, N = 3. Types of faults are chosen as network outputs, thus M = 7.

Therefore, number of nodes in the input and output layers are determined. The number of

hidden neural nodes is decided accordingly be 10. Then raw data out of 1236 samples are

used for training the patterns to train this neural network with the desired error goal set to

0.0. After successful training, the weight matrices and biases are stored as files. The final

number of hidden nodes is confirmed to be 11 and the 3-11-7 ‗network topology‘ is fixed.

Step 4; ANN result outputs A two-layer BP-ANN with 53 epochs is designed and

trained by different training patterns obtained from different power transformers‘ fault

diagnosis reports having capacities from 6.3-250 MVA, 11/0.4-420/21 KV collected from

various organizations/utilities in INDIA. From the MATLAB environment, it is seen that

from Fig. 6.28 that best validation performance is 0.49576 at epoch 47 out of 53 epochs.

181

Figure 6.28: Validation performance chart

Similarly, from training states, the Mu (the momentum) for the weights and biases is set

at 1, and also the gradient (local minima) is 0.0051068 can be observed from Fig. 6.29

(a). Though output goal is set to be 0 and other input /output targets for this study are

given in Fig. 6.29 (b).

Figure 6.29 (a) Mu and Gradient plot Figure 6.29 (b) Target plots

Taking all above details, for this thesis work a string of 1236 input samples between the

range of 0-100% in the variations of 0.1%, 0.2%, 0.25% and 0.5% as per requirements

were developed for three input vectors. This string of sample vectors is used for feed-

forward BP-ANN Duval triangle 1 diagnostic method.

6.11 ANN APPROACH FOR DTM 2

Considering Fig. 5.10 of Duval triangle 2 for OLTCs filled with transformer oil, the

procedure is as follows;

182

Step 1; Polygon coordinates for the numerical zone boundaries of six key faults of Duval

triangle 2 are listed in Table 5.7 and the same input percentages of numerical zones are

being used to create strings of three gas ratios (CH4, C2H4 and C2H2).

Step 2; Input and output patterns ANN technique used for DTM2 is similar to DTM1.

Which needs three combustible gases methane (CH4), ethylene (C2H4) and acetylene

(C2H2), therefore, for this thesis work, input and output patterns of DTM2 for fault

diagnosis in power transformers are as follows:

[Input] = [x] = [%CH4 %C2H4 %C2H2] T

[0utput] = [O] = [N D1 X1 X3 T2 T3] T


[O] = [1 2 3 4 5 6 ] T

A brief parameter details for this thesis on DGA for fault diagnosis in power transformers

through ANN based Duval triangle 2 approach are shown in Table 6.26.

Table 6.26: Parameters for ANN Duval triangle 2


Output Faults [1 2 3 4 5 6]T

Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 175



Functions











Epochs 1000

Time Infinite

Goal 0


forward network similar to DTM1 is used and complete configuration and training of

two-layer ANN in MATLAB environment is shown in Fig. 6.30.

183


There are 840 samples as faults string are used for training the neural network in DTM2.

The network topology is fixed to be 3-11-6 in DTM2.


trained with different training patterns. From Fig. 6.31, it is noticed that best validation

performance is 0.41116 at epoch 167 out of 175 epochs.


The gradient (local minima) is 0.029756, Mu 0.1 and validation checks 06 can be

observed from Fig. 6.32 (a) and output goal is set to be 0 and other input /output targets

are given in Fig. 6.32 (b).

184

Figure 6.32 (a): Mu and Gradient plot Figure 6.32 (b): Target plots

For DTM2 fault analysis purposes, a string of 840 fictitious input samples between the

range of 0-100% in the variations of 0.1%, 0.2%, 0.25% and 0.5% are used to develop

three input vectors.

6.12 ANN APPROACH FOR DTM3

Since there are four Duval triangles are available for four synthetic oils (silocone, midel,

FR3 and biotemp oils). For this thesis work each DTM3 is considered separately and

analysed for fictitious input samples because synthetic oils are not popular transformer

oils in India.

6.12.1 ANN Approach for DTM3, Silicone Oil

From Fig. 5.13 of Duval triangle 3 for non-mineral silicone oil, the ANN approach is as

follows;


Duval triangle 3, silicone are listed in Table 5.10 and the same input percentages of


Step 2; Input and output patterns ANN technique used for DTM3 is similar to DTM1

and DTM2. Which needs three combustible gases methane (CH4), ethylene (C2H4) and

acetylene (C2H2), therefore, for this thesis work, input and output patterns of DTM3 are

equal to DTM1 for fault diagnosis in power transformers are as follows:

[Input] = [x] = [%CH4 %C2H4 %C2H2] T



[O] = [1 2 3 4 5 6 7] T

185


transformers through ANN based Duval triangle 3, silicone oil approach are shown in

Table 6.27.

Table 6.27: Parameters for ANN Duval triangle 3, silicone oil



Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 116



Functions











Epochs 1000

Time Infinite

Goal 0


forward network similar to DTM1 is used and complete configuration and training of

two-layer ANN in MATLAB environment is shown in Fig. 6.33.

186

Figure 6.33: Two-layer BP-ANN for DTM3, silicone oil

There are 1640 samples as faults string are used for training the neural network in DTM3,

silicone oil. The network topology is fixed to be 3-11-7 in DTM3, silicone oil.








187


For DTM3, silicone oil fault analysis purposes, a string of 1640 fictitious input samples

between the range of 0-100% in the variations of 0.1%, 0.2%, 0.25% and 0.5% are used

to develop three input vectors.

6.12.2 ANN Approach for DTM3, Midel Oil

From Fig. 5.13 of Duval triangle 3 for non-mineral midel oil, the procedure for ANN

approach is as follows;


Duval triangle 3, midel oil are listed in Table 5.15 and the same input percentages of






[Input] = [x] = [%CH4 %C2H4 %C2H2] T



[O] = [1 2 3 4 5 6 7] T


transformers through ANN based Duval triangle 3, midel oil approach is shown in Table

6.28.

Table 6.28: Parameters for ANN DTM 3, Midel oil



188

Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 78



Functions











Epochs 1000

Time Infinite

Goal 0 Step 3; Neural Network configurations and training For this thesis two-layer feed-

forward network similar to DTM1 and DTM2 are used and complete configuration and

training of two-layer ANN in MATLAB environment is shown in Fig. 6.36.

Figure 6.36: Two-layer BP-ANN for DTM3, silicone oil

189


silicone oil. The network topology is fixed to be 3-11-7 in DTM3, midel oil.









For DTM3, silicone oil fault analysis purposes, a string of 1630 fictitious input samples



190

6.12.3 ANN Approach for DTM3, FR3 Oil

From Fig. 5.13 of Duval triangle 3 for non-mineral FR3 oil, the procedure for ANN



Duval triangle 3, FR3 oil are listed in Table 5.17 and the same input percentages of






[Input] = [x] = [%CH4 %C2H4 %C2H2] T



[O] = [1 2 3 4 5 6 7] T


transformers through ANN based Duval triangle 3, FR3 oil approach is shown in Table

6.29.

Table 6.29: Parameters for ANN Duval triangle method 3, FR3 oil



Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 120



Functions











Epochs 1000

Time Infinite

Goal 0

191




Figure 6.39: Two-layer BP-ANN for DTM3, FR3 oil


silicone oil. The network topology is fixed to be 3-11-7 in DTM3, FR3 oil.




192






For DTM3, FR3 oil fault analysis purposes, a string of 1625 fictitious input samples



6.12.4 ANN Approach for DTM3, BioTemp Oil

From Fig. 5.13 of Duval triangle 3 for non-mineral BioTemp oil, the procedure for ANN





193





[Input] = [x] = [%CH4 %C2H4 %C2H2] T



[O] = [1 2 3 4 5 6 7] T


transformers through ANN based Duval triangle 3, biotemp oil approach is shown in

Table 6.30.

Table 6.30: Parameters for ANN Duval triangle method 3, BioTemp oil



Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 343



Functions











Epochs 1000

Time Infinite




194

Figure 6.42: Two-layer BP-ANN for DTM3, BioTemp oil


silicone oil. The network topology is fixed to be 3-11-7 in DTM3, biotemp oil.




195


The gradient (local minima) is 0.11522, Mu 0.1 and validation checks 06 can be observed

from Fig. 6.44 (a) and output goal is set to be 0 and other input /output targets are given

in Fig. 6.44 (b).


For DTM3, biotemp oil fault analysis purposes, a string of 1630 fictitious input samples




DTM 4 is used for low-energy fault diagnosis in power transformers in addition to

DTM1. Three gases used in DTM 4 are H2, CH4 and C2H6. As shown in Fig. 5.18, Duval

triangle 4 has four types of faults for mineral oil in main tank, the procedure for ANN


196




Step 2; Input and output patterns ANN technique used for DTM4 is similar to other

DTMs. Which needs three combustible gases hydrogen (H2), methane (CH4) and ethylene

(C2H6), therefore, for this thesis work, input and output patterns of DTM4 are equal to

DTM1 for fault diagnosis in power transformers are as follows:

[Input] = [x] = [%H2 %CH4 %C2H6] T

[0utput] = [O] = [PD S C O] T


[O] = [1 2 3 4] T


transformers through ANN based Duval triangle 4 approach is shown in Table 6.31.

Table 6.31: Parameters for ANN Duval triangle method 4

Input [%CH2 %CH4 %C2H6]T

Output Faults [1 2 3 4]T

Maximum Input

Value

100%, 100%, 100%

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 61



Functions











Epochs 1000

Time Infinite




197








198






DTM 5 is used for low-temperature fault diagnosis in power transformers in addition to

DTM1. Three gases used in DTM 5 are CH4 C2H4 and C2H6. As shown in Fig. 5.20,

Duval triangle has five types of faults for mineral oil in main tank, the procedure for

ANN approach is as follows;


Duval triangle 5 are listed in Table 5.27 and the same input percentages of numerical

zones are being used to create strings of three gas ratios.

Step 2; Input and output patterns ANN technique used for DTM5 is similar to other

DTMs. Which needs three combustible gases methane (CH4), ethane (C2H4) and ethylene

(C2H6), therefore, for this thesis work, input and output patterns of DTM4 are equal to

DTM1 for fault diagnosis in power transformers are as follows:

[Input] = [x] = [%CH4 %C2H4 %C2H6] T

[0utput] = [O] = [PD S C O T3] T


[O] = [1 2 3 4] T


transformers through ANN based Duval triangle 5 approach is shown in Table 6.32.

Table 6.32: Parameters for ANN Duval triangle 5

Input [%CH2 %CH4 %C2H6]T

Output Faults [1 2 3 4]T

Maximum Input

Value

100%, 100%, 100%

199

Minimum Input

Value

0.1%, 0.1%, 0.1%


Epochs 42



Functions











Epochs 1000

Time Infinite

Goal 0


forward network similar to DTM1, DTM2, DTM3s and DTM4 are used. Complete

configuration and training of two-layer ANN in MATLAB environment is shown in Fig.

6.48.


200







The gradient (local minima) is 2.548, Mu 10 and validation checks 06 can be observed

from Fig. 6.50 (a) and output goal is set to be 0 and other input /output targets are given

in Fig. 6.50 (b).


Selected samples of DGA fault reports (APPENDIX-B) by other conventional diagnostic

methods are used for neural network training in different groups. Their vector input

ranges were rounded-up to the first digit of decimal (0.50) to match the variations in

increasing/decreasing trend. Many unknown new input vectors were also used for neural

testing. Neural network does not know these vectors. The result table presented for

201

known faults which is cross verified with other software schemes with this new method

shows that neural network efficiency for this set of input vector is in the range of 90-

100%, which is quite acceptable.

Results using FL and ANN approaches and fault diagnosis reports available from various

organizations gives the following conclusions:

Percentage of gases based rules in FL gives flexibility in Duval triangle methods

to get accurate analysis. Results obtained from fuzzy logic approach on DTM1 are

compared with simulation results shown in Table 6.33. It is found that FLA gives better

results as compared to the conventional method results e.g. for 250 and 315 MVA

transformers conventional methods provides the range of temperatures while FLA

provides particular fault.

Table 6.33: Comparative results of DTM1 simulation and Fuzzy logic DTM1

Sr.

No

Transformer

Ratings

GASES

(ppm)

GASES (%)

DGA Fault Diagnosis/ Comments

DTM 1

RESULTS

MVA KV CH4 C2H4 C2H2 CH4 C2H4 C2H2 CEBG

IEC,

CIGRE

IEEE/R

eco.

Comnt

s Fuzzy logic

1 6.3 33/11 2 28 0 6.67 93.33 0.00

OH OH T32

2 16 132/33 3 64 42 2.75 58.72 38.53

OH OH D22

3 16/20 132/33 17 17 0 50.00 50.00 0.00

T OH T33

4 200 420/21 1 1 0 50.00 50.00 0.00 PD NF N IG T22

5 250 16/420 40 10 0 80.00 20.00 0.00

200-

300°C NF

200-

300°C

Ind.

Gases T12

6 315 400/220 79 89 0 47.02 52.98 0.00

300-700

°C <700°C

T21

Superior learning capabilities of ANN gives better and quicker analysis as

compared to other fault diagnosis methods. Results obtained from ANN approach on

DTM1 compared with simulation results are shown in Table 6.34.

Table 6.34: Comparison of results simulation and ANN

S.No.

Transformer Rating GASES

(ppm)

GASES (%)


DUVAL

RESULTS


IEC,

CIGRE IEEE/Reco. Comnts ANN

1 10/12.5 66-33/114 6852 3137 ND 68.50 31.50 ND

C2H2 High Old OIL OH T2

2 100 220/66 187 614 ND 23.50 76.50 ND

C2H4 High Hot spot

T3

3 250 16/420 40 10 0 80.00 20.00 0.00

200-

300°C NF

200-300°C

Ind.

Gases D1

202

4 200 420/21 1 1 0 50.00 50.00 0.00 PD NF N IG T2

5 315 400/200 55 72 0 43.50 56.50 0.00

300-700 °C >700°C OH T3

Further, it is also found that ANNA gives better results as compared to the conventional

method results e.g. for 100 and 315 MVA transformers conventional methods provides

the range of temperatures while ANNA provides particular fault in less time.

Simulated results using fuzzy logic and artificial neural network approach on Duval

triangle method1 are compared with fault diagnosis reports as shown in Table 6.35.

Table 6.35: Comparison of FL and ANN results

Sr.

No.

Transf0rmers

Ratin

gs

GASES

(ppm)

GASES (%)


DTM 1

RESULTS


IEC,

CIGRE

IEEE/

Reco. Comnts. ANN

Fuzzy

logic

1. 6.3 33/11 2 28 0 6.67 93.33 0.00

OH OH T3 T32

2. 10/12.5

66-

33/114

685

2 3137 ND 68.50 31.50 ND

C2H2

High

Old

OIL OH T2 T2

3. 16/20 132/33 17 17 0 50.00 50.00 0.00

T OH T3 T33

4. 100 220/66 187 614 ND 23.50 76.50 ND

C2H4

High

Hot

spot

T3 T3

5. 200 420/21 1 1 0 50.00 50.00 0.00 PD NF N IG T2 T22

6 315

400/22

0 79 89 0 47.02 52.98 0.00

300-700

°C <700°C

T2 T21

It is observed that ANNA provides quick and accurate results once trained properly. This

capability of ANNA will be very useful for complete fault diagnosis of power

transformers.

6.15 SUMMARY

Simulated results with fuzzy logic and artificial neural network approaches on Duval

triangle 1 are compared with fault diagnosis report results. It is found that fuzzy logic and

artificial neural network approaches will give better results than actual fault diagnosis

report results. Further it is it is observed that ANN provides fast and accurate results as

compared to fuzzy logic result.

203

CHAPTER 7

CONCLUSIONS AND FUTURE SCOPE OF WORK

7.1 CONCLUSIONS

This chapter includes the conclusions and the suggestions for future work. Major

conclusions are as follows;

Critical specifications of transformer oil can be performed for fault

diagnosis/condition monitoring (periodic test) as pre-warnings of the faults (if any) after

thermal degradation of transformer oil test analysis.

Type of fault and reasons for the faults are correlated to confirm the faults from

the test reports of critical specifications of oil.

Most of the solutions are regeneration of oil such as filtering, removal of water


After comparison of different fault diagnosis methods of power transformers, it is

found that Duval Triangle Methods (DTMs) gives better results.

DTMs have satisfied the fault diagnosis for more than 95% accuracy compared to

the other fault diagnosis methods.

Traces of one of the three gases can be helpful for early fault indication in the

power transformers.

Application of FL and ANN in DTMs can provide higher accuracy, low

uncertainty and quicker analysis in fault diagnosis of power transformers.

It has been found that ANN based DTMs can provide full solutions in fault


7.2 FUTURE SCOPE OF WORK

This thesis gives following suggestions for future scope of work.

Optimization techniques may be attempted for improvement of accuracy in fault


Intelligent techniques may be applied to reduce the uncertainties among faults.

204

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214

APPENDIX-A

CASE STUDY 1: 650 MVA, 400/220 KV, Transformer

DGA result [115] for gas concentration on 11th

November 2001 shown in Table A-1.

Table A-1: Gas concentrations

Analysis:

Routine DGA during six month period, the above mentioned analysis showed no

significant changes in fault gas concentrations. However the decision was made to

increase the DGA testing frequency and to install an online hydrogen and water monitor

on the suspect transformer.

Monitoring equipments were installed in May, 2002 and decision was made to degas the

transformer in order to reset the dissolved gas reference to zero.

Hydrogen generation rate from 30th

May to 26th

September 2002 are given in Table A-2.

Table A-2: Gas generating rate

Time Period H2 Generating Rate

(ppm/day)

May 3 to May 20 <1.0

May 30 to June 15 3.8

June 16 to June 28 5.0

June 29 to Aug. 12 2.8

Aug. 13 to Aug. 25 4.1

Aug. 26 to Sept. 12 1.4

Sept. 13 to Sept. 26 12.1

Dissolved Gases Gas Concentration (ppm)

Hydrogen 392

Methane 594

Ethane 260

Ethylene 260

Acetylene N/D (Not Defined)

Carbon monoxide 736

Carbon dioxide 5900

215

From the above data, it can be observed that the hydrogen-generating rate significantly

increased on 13th

September, thus indicating a sudden change in gas generating

conditions.

On site DGA performed during the period are shown in Table A-3.


Dissolved Gases May 30, 2002 July 8, 2002 Sept. 17 , 2002

Hydrogen N/D 170 419

Methane N/D 372 865

Ethane N/D 184 505

Ethylene N/D 179 528

Acetylene N/D 35 30

Carbon monoxide 4 160 262

Carbon dioxide 25 1360 2726

Though large increase in fault gas concentration was observed, percent changes between

tests for both absolute concentrations and key ratios remained more or less constant.

H2 generating rates between 27th

September and 29th

September, 2002 are listed in Table

A-4.

Table A-4: H2 generating rate

Time Period H2 Concentration

(ppm)

Max. H2 Generating

Rate (ppm/hour)

Sept.27,2002,5:52 to Sept.28,2002,5:53 1367 100

Sept.28,2002,8:53 to Sept.28,2002,23:42 5006 300

Sept.29,2002,11:43 to Sept.29,2002,23:42 9494 392

Analysis by Ratio method: (Modified Roger’s Ratio-Fault Diagnosis) from the Table

A-5.

Table A-5: Roger‘s Ratio fault diagnosis

Dissolved Gases July 8, 2002 Sept. 17 , 2002

Hydrogen 170 419

Methane 372 865

Ethane 184 505

216

Ethylene 179 528

Acetylene 35 30

Carbon monoxide 160 262

Carbon dioxide 1360 2726

On 17/9/02 the results are:

R2= C2H2/C2H4 = 60/528 = 0.1136, R2 (0.1-3.0)

R1= CH4/H2 = 865/419 = 2.0644, R1 (>1.0)

R5=C2H4/C2H6 = 528/505=1.0455, R5 (>1.0)

After analyzing the data contained in the above tables, a diagnostic of thermal fault of

medium range temperature was established from 8th

July to 17th

September.

DGA results 30th

September 2002. Fault analysis by Dornenburg‘s ratio of Table A-6.

Table A-6: Dornenburg‘s ratio fault diagnosis

Dissolved Gases Concentration (ppm)

Hydrogen 8150

Methane 11000

Ethane 2460

Ethylene 17960

Acetylene 570

Carbon monoxide 351

Carbon dioxide 2620

R2= C2H2/C2H4 = 570/17960 = 0.0317, R2 (< 0.1)

R1= CH4/H2 = 11000/8150 = 1.3496, R1 (>1.0)

R5=C2H4/C2H6= 17960/2460 =7.3008, R5 (>3.0)

Test results of 30th

September data then led to high temperature (> 700°C) thermal fault

diagnostic.

Analysis by Duval Triangle; on the basis of gas concentrations on 17th

September, 2002.

Concentration of CH4 = 865

Concentration of C2H4 = 528


Sum of (CH4 + C2H4 + C2H2) = 865+528+30= 1423

% CH4 = 100 (865/1423) = 60.78

% C2H4 = 100 (528/1423) = 37.10

217

% C2H2 = 100 (30/1423) = 2.108

Plotting the coordinates in the triangle, a thermal fault (300 °C-700 °C) is located.

On 30/9/02:



Concentration of C2H2 =570

Sum of (CH4 + C2H4 + C2H2) = 11000+17960+570= 29530

% CH4 = 100 (11000/29530) = 37.25

% C2H4 = 100 (17960/29530) = 60.81

% C2H2 = 100 (570/29530) = 1.93

Plotting the coordinates in the triangle a thermal fault >700 °C is located.

At some stage between 17th

September and 27th

September additional energy, possibly

due to circulating currents, increased significantly the temperature of metal and oil at the

fault location. This led to accelerated production of hydrogen, methane, ethylene and a

small relative amount of acetylene, which are indicative of overheating of oil above 700

°C.

From these diagnostics one can suspect that a source of high electrical resistance was

initially present in transformer (e.g. a bad connection). This high resistance created a

resistive hot spot of increasing resistance, thus the observed increase in hydrogen,

methane, ethane and ethylene. The transformer was taken out of service; it was found that

a high voltage lead was burned and ready to fail.

CASE STUDY 2- 105 MVA, 400/220/33 KV, 1Φ, Autotransformer

The transformer was commissioned on 18.7.97 showed fault gases in its DGA results,

which are tabulated in Table A-7 below:

Table A-7: Gas concentration

Dissolved Gases July28,1997 Aug.14, 1997 Aug.24, 1997

Hydrogen 70 181 319

Methane 09 24 38

Ethane 2 2 2

Ethylene 11 23 33

Acetylene 32 104 134


218


Analysis by Ratio method: (Modified Roger’s Ratio-Fault Diagnosis)

On August 24, 1997:

R2= C2H2/C2H4 = 134/33 =4.06, R2 >3

R1= CH4/H2 = 38/319 = 0.12, R1 >0.1 & R1 < 1.0

R5=C2H4/C2H6 = 33/2 =16.5, R5 > 3

DGA result indicates definite arcing inside the transformer as the acetylene increased

from 32 to 104 ppm with in fortnight. The IEC 60599 analyses interpreted the fault as

arcing with discharge of low energy due to continuous sparking in oil between bad

connection of different potential or to floating potential. Roger‘s ratio method also

analyses as low energy fault with continuous sparking in floating potential.

The first internal inspection by manufacturer did not conclusively reveal the source of the

arcing fault within transformer. The transformer was recharged and fault gases started

rising in the subsequent monthly DGA samples as shown below:

Analysis by Ratio method: (Modified Roger’s Ratio, Fault Diagnostic)

On 24th

April, 1998:

R2 = C2H2/C2H4 = 106/24 =4.416, R2 >3

R1 = CH4/H2 = 33/450 = 0.068, R1 < 1.0

R5 = C2H4/C2H6 = 24/2 = 12.0, R5 > 3

Test results by Roger‘s Ratio method did not conclusively reveal the type of fault as the

DGA results were falling outside the ratio codes. But rise of acetylene interpreted the

fault as arcing with discharge of low energy.

Analysis by Duval Triangle; on the basis of gas concentrations on 24th

August 1998.




Sum of (CH4 + C2H4 + C2H2) = 33+24+106= 163

% CH4 = 100 (33/163) = 20.24

% C2H4 = 100 (24/163) = 14.72

% C2H2 = 100 (106/163) = 65.03

Plotting the coordinates in the Duval triangle, the fault is located in the area of low energy

discharge (small arcing). Hence Duval triangle method help to diagnose the fault as it is a

closed system rather than open one.

219

Observing the fault gas pattern from Fig. A-8, it was interpreted that fault does not

originate from the winding. The fault/arcing appears to be originated from any metallic

joint within the transformer. During the 2nd internal inspection the wall shunts clamping

structure was found loose with arcing marks on the earth strips and the boss. No other

abnormality was noticed inside the transformer.


Dissolved Gases 10th

March 1998 4th

April 1998 13th

April 1998

Hydrogen 11 317 450

Methane 4 30 33

Ethane 1 2 2

Ethylene 7 24 24

Acetylene 8 121 106



220

APPENDIX-B

Case 1: 100MVA, 220/66kV Power Transformer:

Gases Sept. 2004 Jun. 2005 Jul. 2005 As per

IEC

C2 H4

High

As IEEE

Std…

HOT

SPOT

Methane 18 63 187

Ethane 15 48 178

Ethylene 18 194 614

Acetylene ND ND ND

Carbon Dioxide 155 582 2034

Carbon mono-oxide ND ND ND

Hydrogen ND ND ND

Total gas content (TGC) 432 543 765

Case 2: 20MVA, 11/0.4kV Power Transformer:

Gases s-1 s-2 s-3 s-4 s-5 s-6

AS per

IEC

C2H2 High

As per

IEEE Std..

Overloading

fault

Methane 3 5 4 9 9 10

Ethane 1 ND ND 2 2 2

Ethylene 5 7 9 14 18 23

Acetylene ND ND 3 5 9 11

Carbon Dioxide 340 409 439 1419 603 733

Carbon mono-oxide ND ND ND ND ND ND

Hydrogen ND ND ND ND ND ND

Total gas content (TGC) 61 765 588 876 743 765

Case 3: 10/12.5MVA, 66-33/114kV Power Transformer:

Gases 12.09.05 23.09.05 17.10.05 27.10.05 25.11.05 29.11.05

As per Methane 2779 2738 1222 34 392 6852

221

Ethane 1330 1245 1333 30 299 3137 IEC

C2H4

High

IEEE

As per

OLD

OIL

Ethylene 3359 3102 2887 55 879 8797

Acetylene 11 13 15 ND ND ND

Carbon

Dioxide

1713 3243 3820 97 508 1342

Carbon

mono-

oxide

ND ND ND ND ND ND

Hydrogen 733 620 392 ND 3791 2195

Total gas

content

(TGC)

699 720 943 477 943 765

Date of

Filtration

Jun - 03 Jun-03 Jun-03 23.10.05 23.10.05 23.10.05


Gases 2/15/05 5/13/05 6/28/05 06/07/05 10/07/05

As per

IEC

C2H2

High

As per

IEEE

Std.

HOT

SPOT

Methane 38 102 10 2 254

Ethane 20 60 47 3 17

Ethylene 36 108 141 7 313

Acetylene ND ND 137 5 417

Carbon Dioxide 2382 4399 9109 252 365

Carbon mono-oxide ND ND ND ND ND

Hydrogen ND ND 930 ND 328

Total gas content (TGC) 605 820 699 432 521

Date of Filtration 1988/-- 1988/-- 1988/-- 1988/--

06.07.05

1988/--

06.07.05


Gases 11/05/05 14/06/05 30/06/05 10/07/05 05/08/05

As per Methane 5 36 51 86 83

222

Ethane ND 6 7 11 10 IEC

C2H4

High

As per

IEEE

HOT

SPOT

Ethylene 17 106 145 213 221

Acetylene ND ND 4 9 3

Carbon Dioxide 899 499 1193 1214 1819

Carbon mono-

oxide

ND ND ND ND ND

Hydrogen ND ND 8 9 13

Total gas content

(TGC)

743 92 543 654 61

Date of Filtration 2002/-- 2002/--

25/5/05

2002/--

25/3/05

2002/--

25/3/05

2002/--

25/3/05


Parameter Measured Value

As per IEC

300-700 oC

As per IEEE

Above 700 oC

Total Gas content 6.85

Nitrogen(N2) 4.90

Oxygen(O2) 1.11

Hydrogen()H2 39

Methane(CH4) 55

Ethylene(C2H4) 72

Ethane(C2H6) 22

Acetylene(C2H2) 0.0

Carbon mono-oxide(CO) 80

Carbon Dioxide(CO2) 2328


Parameter Measured Value

Total Gas content 9.40

223

Nitrogen(N2) 7.02 As per IEC

300-700 oC

As per IEEE

Below 700 oC

Oxygen(O2) 1.51

Hydrogen(H2) 40

Methane(CH4) 79

Ethylene(C52H4) 89

Ethane(C2H6) 42

Acetylene(C2H2) 0.0



Case 8: 250MVA Generator Transformer:

Gas 18/1/01 19/4/01 10/7/01

IEC

300-700 oC

As per

IEEE Std.

Circulating

Current

TGC 9.52 9.52 10.87

Methane 547 631 362

Ethane 199 386 217

Ethylene 530 924 512

Acetylene ND ND ND

Hydrogen 18 81 54

Carbon Dioxide 2905 3449 4155

Case 9: 25MVA Transformer:

Gas Contents

As per IEC

NO FAULT

As per IEEE

Arc Discharge

Methane 20

Ethane 10

Ethylene 21

Acetylene 47

Hydrogen 139

224

Case 10: 50MVA Transformer:

Gas 9/5/05 2/9/05

AS per IEC

NO FAULT

As per IEEE Std.

Arc Discharge

Methane 748 772

Ethane 238 182

Ethylene 2587 1936

Acetylene 10 1

Hydrogen 198 149

Case 11: 50MVA, 400kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults

Total Combustible

Gas

438 CEGB METHOD: Circulating current-bad

contact.

IEC, CIGRE STANDARDS: Thermal fault

of medium temperature range 300-700 oC.

Local overheating of core due to

concentration of flux. Increasing hot spot

temperatures varying from small hot pots in

core, shorting links in core, overheating of

copper due to eddy currents bad

contacts/joints upto core & tank circulating

currents.

ANSI/IEEE STANDARDS: Circulating

currents in windings.

REMARKS: DGA show C2H4 level being

marginal wrt IEEE norms (50). C2H4,

however has not shown increase wrt earlier

sample. Next sample is due after 3 months

for further monitoring.

Hydrogen (H2) 42

Methane(CH4) 108

Ethylene(C2H4) 50

Ethane(C2H6) 37

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

196


Nitrogen (N2 %) 4.20

Oxygen (O2 %) 0.00

CH4/H2 2.57

C2H6/CH4 0.34

C2H4/C2H6 1.35

C2H2/C2H4 0.00

Case 12: 200MVA, 400/21kV, Power Transformer (2005):

225

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 400 CEGB METHOD: Circulating current-bad

contact.

IEC, CIGRE STANDARDS: Thermal fault of

high temperature range >700 oC. Local

overheating of core due to concentration of flux.

Increasing hot spot temperatures varying from

small hot pots in core, shorting links in core,

overheating of copper due to eddy currents bad


currents.

ANSI/IEEE STANDARDS: Circulating currents

in windings.

REMARKS: DGA show C2H4 level is exceeding

IEEE norms (50). C2H4, however has not shown

increase wrt earlier sample. Next sample is due

after 1 month for further monitoring.

Hydrogen (H2) 45

Methane(CH4) 80

Ethylene(C2H4) 120

Ethane(C2H6) 35

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

90



Oxygen (O2 %) 0.00

CH4/H2 1.78

C2H6/CH4 0.44

C2H4/C2H6 3.43

C2H2/C2H4 0.00

Case 13: 250MVA, 16/420kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 310 CEGB METHOD: Overheating 200-300 oC..

IEC, CIGRE STANDARDS: No fault

normal ageing.

ANSI/IEEE STANDARDS: Overheating

200-300 oC.

REMARKS: DGA show individual gases

within IEEE norms (50). Next sample is

due after 3 months for further monitoring.

Hydrogen (H2) 52

Methane(CH4) 40

Ethylene(C2H4) 10

Ethane(C2H6) 48

Acetylene(C2H2) 0




226

Oxygen (O2 %) 0.00

CH4/H2 0.77

C2H6/CH4 1.20

C2H4/C2H6 0.21

C2H2/C2H4 0.00

Case 14: 200MVA, 400/21kV, Power Transformer(2005):

Parameter Measured

Value(ppm)

Faults

Total

Combustible

Gas

425 CEGB METHOD: Circulating current-bad contact.

IEC, CIGRE STANDARDS: Thermal fault of high

temperature range >700 oC. Local overheating of core

due to concentration of flux. Increasing hot spot

temperatures varying from small hot pots in core,

shorting links in core, overheating of copper due to

eddy currents bad contacts/joints upto core & tank

circulating currents.

ANSI/IEEE STANDARDS: Circulating currents core

& tank; over loaded joints.

REMARKS: DGA show C2H4 level is exceeding IEEE

norms (50). C2H4, however has not shown increase

wrt earlier sample. Next sample is due after 1 month

for further monitoring.

Hydrogen (H2) 50

Methane(CH4) 90

Ethylene(C2H4) 125

Ethane(C2H6) 30

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

95

Carbon

Dioxide(CO2)

960


Oxygen (O2 %) 0.009

CH4/H2 1.80

C2H6/CH4 0.33

C2H4/C2H6 4.17

C2H2/C2H4 0.00

227

Case 15: 250MVA, 16/420 kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 185 CEGB METHOD: Overheating below 150 oC.

IEC, CIGRE STANDARDS: Thermal fault of low

temperature range 150-300 oC. Increasing hot spot

temperatures varying from small hot pots in core,

shorting links in core, overheating of copper due to

eddy currents bad contacts/joints upto core & tank

circulating currents.

ANSI/IEEE STANDARDS: Slight overheating to

150 oC.

REMARKS: Sample taken after oil filtration and

recharging: present DGA results may be kept as

benchmark for further monitoring. Next sample is

due after 1 month.

Hydrogen (H2) 25

Methane(CH4) 30

Ethylene(C2H4) 0

Ethane(C2H6) 15

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 1.20

C2H6/CH4 0.50

C2H4/C2H6 0.00

C2H2/C2H4 ****

Case 16: 50MVA, 420 kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults

Total

Combustible

Gas

340 CEGB METHOD: Overheating 150-200 oC.

IEC, CIGRE STANDARDS: Thermal fault of low

temperature range 150-300 oC. Local overheating

of core due to concentration of flux. Increasing hot

spot temperatures varying from small hot pots in

core, shorting links in core, overheating of copper

due to eddy currents bad contacts/joints upto core

& tank circulating currents.

ANSI/IEEE STANDARDS: Slight overheating

150-200 oC.

Hydrogen (H2) 20

Methane(CH4) 25

Ethylene(C2H4) 10

Ethane(C2H6) 65

Acetylene(C2H2) 0

228

Carbon mono-

oxide(CO)

190 REMARKS: DGA results show C2H6 level being

marginal wrt IEEE norms.. Next sample is due

after 3 months for further monitoring. Carbon

Dioxide(CO2)

2310


Oxygen (O2 %) 0.00

CH4/H2 1.25

C2H6/CH4 2.60

C2H4/C2H6 0.15

C2H2/C2H4 0.00

Case 17: 63MVA, 420 kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 556 CEGB METHOD: Overheating below 150 0C.


low temperature range 150-300 oC. Local

overheating of core due to concentration of flux.

Increasing hot spot temperatures varying from

small hot pots in core, shorting links in core,

overheating of copper due to eddy currents bad


currents.

ANSI/IEEE STANDARDS: Slight overheating

to 150 oC.

REMARKS: DGA results show C2H6 level

exceeding IEEE norms. Next sample is due after

1 month.

Hydrogen (H2) 45

Methane(CH4) 105

Ethylene(C2H4) 15

Ethane(C2H6) 66

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

310



Oxygen (O2 %) 0.00

CH4/H2 2.33

C2H6/CH4 0.63

C2H4/C2H6 0.23

C2H2/C2H4 0

229

Case 18: 200 MVA, 400/21kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 225 CEGB METHOD: Partial Discharge.

IEC, CIGRE STANDARDS: No fault-

Normal Ageing.

ANSI/IEEE STANDARDS: Normal

REMARKS: DGA results show gases well

within IEEE norms. Next sample is due after

3 months.

Hydrogen (H2) 30

Methane(CH4) 2

Ethylene(C2H4) 2

Ethane(C2H6) 9

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.07

C2H6/CH4 4.50

C2H4/C2H6 0.22

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults



Normal Ageing.


REMARKS: DGA results show individual

gases well within IEEE norms. Next sample

is due after 3 months for further monitoring.

Hydrogen (H2) 34

Methane(CH4) 1

Ethylene(C2H4) 1

Ethane(C2H6) 1

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

50

230


Nitrogen (N2 %) 4,30

Oxygen (O2 %) 0.00

CH4/H2 0.03

C2H6/CH4 1.00

C2H4/C2H6 1.00

C2H2/C2H4 0.00


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 187 CEGB METHOD: Sample is o.k.


Normal Ageing.


REMARKS: DGA results show gases well

within IEEE norms. Next sample is due

after 3 months for further monitoring.

Hydrogen (H2) 48

Methane(CH4) 20

Ethylene(C2H4) 7

Ethane(C2H6) 5

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.42

C2H6/CH4 4.30

C2H4/C2H6 1.40

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults

231


IEC, CIGRE STANDARDS: No fault-Normal

Ageing.


REMARKS: DGA results do not show any

abnormality. Next sample is due after 6 months.

Hydrogen (H2) 30

Methane(CH4) 1

Ethylene(C2H4) 6

Ethane(C2H6) 2

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.03

C2H6/CH4 4.40

C2H4/C2H6 3.0

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 310 CEGB METHOD: sample is o.k.


Ageing.



abnormality. Next sample is due after 6 months.

Hydrogen (H2) 35

Methane(CH4) 31

Ethylene(C2H4) 10

Ethane(C2H6) 19

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

232

CH4/H2 0.89

C2H6/CH4 0.61

C2H4/C2H6 0.53

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 279 CEGB METHOD: sample is o.k...


Ageing.



abnormality. Next sample is due after 2months.

Hydrogen (H2) 35

Methane(CH4) 4

Ethylene(C2H4) 1

Ethane(C2H6) 5

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.11

C2H6/CH4 1.25

C2H4/C2H6 0.20

C2H2/C2H4 0

Case24: 80 MVA, 132/11kV, Power Transformer (2005):

Parameter Measured

Value(ppm)

Faults



Normal Ageing.


Hydrogen (H2) 5

Methane(CH4) 0

Ethylene(C2H4) 0

233

Ethane(C2H6) 0 REMARKS: sample is taken after oil

filtration/prior to first charging: BDV,

Moisture, & Tan-Delta are as per IS-1866

requirements. SP. resistance value is on

the lower side wrt requirements ( 6E21

min.). Sample is due immediately after

charging.

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.03

C2H6/CH4 ****

C2H4/C2H6 ****

C2H2/C2H4 *****


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 301 CEGB METHOD: sample is o.k..


Normal Ageing.



abnormality. Next sample is due after 3

months.

Hydrogen (H2) 45

Methane(CH4) 20

Ethylene(C2H4) 3

Ethane(C2H6) 3

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.44

C2H6/CH4 0.15

C2H4/C2H6 1.0

C2H2/C2H4 0

234


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 107 CEGB METHOD: Sample is o.k..


Normal Ageing.


REMARKS: DGA results do not show

any abnormality. Next sample is due after

3 months.

Hydrogen (H2) 10

Methane(CH4) 5

Ethylene(C2H4) 1

Ethane(C2H6) 1

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0.50

C2H6/CH4 0.20

C2H4/C2H6 1.00

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults



Ageing.


REMARKS: DGA results show individual gases

well within IEEE norms. Next sample is due after

3 months.

Hydrogen (H2) 36

Methane(CH4) 2

Ethylene(C2H4) 2

Ethane(C2H6) 3

Acetylene(C2H2) 0


235



Oxygen (O2 %) 0.00

CH4/H2 0.06

C2H6/CH4 1.50

C2H4/C2H6 0.67

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults



Normal Ageing.



abnormality. Next sample is due after 6

months.

Hydrogen (H2) 29

Methane(CH4) 10

Ethylene(C2H4) 1

Ethane(C2H6) 1

Acetylene(C2H2) 0




Oxygen (O2 %) 0.00

CH4/H2 0,34

C2H6/CH4 0.10

C2H4/C2H6 1.00

C2H2/C2H4 0

Case29: 75 MVA,400/345kV, Power Transformer (2006):

Parameter Measured

Value(ppm)

Faults

236



Normal Ageing.

Hydrogen (H2) 12

Methane(CH4) 8

Ethylene(C2H4) 5

Ethane(C2H6) 40

Acetylene(C2H2) 0




Oxygen (O2 %) 1.75

CH4/H2 0,67

C2H6/CH4 5.00

C2H4/C2H6 0.13

C2H2/C2H4 0

Case30: 50 MVA,33/6.9kV, Power Transformer (2006):

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 171 CEGB METHOD: Circulating current-bad

contact


medium range 300-700 0C. Local overheating

of core due to concentration of flux. Increasing

hot spot temperature varying from small hot

pots in core, shorting links in , overheating of

copper due to eddy currents bad contacts/joints

upto core & tank circulating currents.

ANSI/IEEE STANDARDS: Circulating

currents in windings.

REMARKS: DGA show C2H4 level being

marginal wrt IEEE norms (50). Next sample is

due after 3 months for further monitoring.

Hydrogen (H2) 16

Methane(CH4) 25

Ethylene(C2H4) 39

Ethane(C2H6) 19

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

70


Nitrogen (N2 %) 00

Oxygen (O2 %) 00

237

CH4/H2 1.56

C2H6/CH4 0.76

C2H4/C2H6 2.05

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults



Normal Ageing.

Hydrogen (H2) 22

Methane(CH4) 3

Ethylene(C2H4) 0

Ethane(C2H6) 0

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

36



Oxygen (O2 %) 2.70

CH4/H2 0.14

C2H6/CH4 00

C2H4/C2H6 ***

C2H2/C2H4 ***

Case32:25 MVA,11/6.6kV, Power Transformer (2006):

Parameter Measured

Value(ppm)

Faults


IEC, CIGRE STANDARDS: No Hydrogen (H2) 33

238

Methane(CH4) 15 fault-Normal Ageing.

Ethylene(C2H4) 0

Ethane(C2H6) 0

Acetylene(C2H2) 0




Oxygen (O2 %) 2.66

CH4/H2 0.45

C2H6/CH4 00

C2H4/C2H6 ***

C2H2/C2H4 ***

Case33: 50MVA,420 kV, Power Transformer (2006):

Parameter Measured Value(ppm) Faults

Total Combustible

Gas

195 CEGB METHOD: Denominator

zero- Diagnosis

IEC, CIGRE STANDARDS: No

fault-Normal

ANSI/IEEE STANDARD :

Denominator zero- Diagnosis

Bypassed

IS:1866(old) NORMS

VOILATION:BDV,

MOISTURE

Hydrogen (H2) 83

Methane(CH4) 5

Ethylene(C2H4) 1

Ethane(C2H6) 0

Acetylene(C2H2) 0

Carbon mono-

oxide(CO)

105

Carbon

Dioxide(CO2)

860


Oxygen (O2 %) 1.66

CH4/H2 0.06

C2H6/CH4 00

239

C2H4/C2H6 ***

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults


IEC, CIGRE STANDARDS: No

fault-Normal Ageing.

ANSI/IEEE STANDARD:

NORMAL

Hydrogen (H2) 13

Methane(CH4) 6

Ethylene(C2H4) 0

Ethane(C2H6) 1

Acetylene(C2H2) 0



Nitrogen (N2 %) 0

Oxygen (O2 %) 0

CH4/H2 0.43

C2H6/CH4 0.17

C2H4/C2H6 0

C2H2/C2H4 ****

Case 35: 50MVA, 33/6.9 kV, Power Transformer (2008):

Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 171 CEGB METHOD: Mixed fault

IEC, CIGRE STANDARDS:

Thermal fault of low temperature

less than 150 oC

- General insulated conductor

overheating

ANSI/IEEE STANDARDS: Mixed

Hydrogen (H2) 32

Methane(CH4) 17

Ethylene(C2H4) 33

Ethane(C2H6) 22

240

Acetylene(C2H2) 0 fault




Oxygen (O2 %) 1.00

CH4/H2 0.53

C2H6/CH4 1.29

C2H4/C2H6 1.5

C2H2/C2H4 0.00


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 311 CEGB METHOD: over heating200-

300 oC


–Normal ageing.

ANSI/IEEE STANDARDS: Over

heating 200-300 oC

Hydrogen (H2) 36

Methane(CH4) 26

Ethylene(C2H4) 2

Ethane(C2H6) 45

Acetylene(C2H2) 0




Oxygen (O2 %) 2.69

CH4/H2 0.72

C2H6/CH4 1.73

C2H4/C2H6 0.07

C2H2/C2H4 0

241


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 448 CEGB METHOD: Conductor

overheating

IEC, CIGRE STANDARDS: Thermal

fault of low temperature less than 150 oC

- General insulated conductor

overheating

- ANSI/IEEE STANDARDS:

General conductor

overheating

Dissolved gas norms violation :co,co2

IS:1866(old) Norms Voilation :

Specific resistance, TAN DELTA

Hydrogen (H2) 30

Methane(CH4) 5

Ethylene(C2H4) 13

Ethane(C2H6) 10

Acetylene(C2H2) 0



Nitrogen (N2 %) 0

Oxygen (O2 %) 0

CH4/H2 0.17

C2H6/CH4 2

C2H4/C2H6 1.3

C2H2/C2H4 0.00

242


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 129 CEGB METHOD: sample OK


–Normal ageing.


CELLULOSE INVOLVEMENT: YES

Hydrogen (H2) 16

Methane(CH4) 5

Ethylene(C2H4) 10

Ethane(C2H6) 2

Acetylene(C2H2) 0



Nitrogen (N2 %) 0

Oxygen (O2 %) 0

CH4/H2 0.31

C2H6/CH4 0.40

C2H4/C2H6 5

C2H2/C2H4 0


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 104 CEGB METHOD: Partial Discharge

IEC, CIGRE STANDARDS: NO fault-

normal Ageing

ANSI/IEEE STANDARDS: normal

CELLULOSE INVOLVEMENT: YES

Hydrogen (H2) 40

Methane(CH4) 3

Ethylene(C2H4) 0

Ethane(C2H6) 1

Acetylene(C2H2) 0


243



Oxygen (O2 %) 2.63

CH4/H2 0.08

C2H6/CH4 0.33

C2H4/C2H6 0

C2H2/C2H4 *****


Parameter Measured

Value(ppm)

Faults

Total Combustible Gas 311 CEGB METHOD: over heating200-300 oC

IEC, CIGRE STANDARDS: No fault –

Normal ageing.

ANSI/IEEE STANDARDS: Over heating

200-300 oC

Hydrogen (H2) 36

Methane(CH4) 26

Ethylene(C2H4) 2

Ethane(C2H6) 45

Acetylene(C2H2) 0




Oxygen (O2 %) 2.69

CH4/H2 0.72

C2H6/CH4 1.73

C2H4/C2H6 0.04

C2H2/C2H4 0

244

PUBLICATION FROM THESIS WORK

Published Papers

[1] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ― Software Implementation of

Duval Triangle Technique for DGA in Power Transformers‖, International Journal of

Electrical Engineering, vol. 4, number 5, pp. 529-540, 2011.

[2] Sukhbir Singh, M. N. Bandyopadhyay, ―Dissolved Gas Analysis Technique for

Incipient fault Diagnosis in Power Transformers: A Bibliographic Survey‖, IEEE

Electrical Insulation Magazine, vol. 26, no. 6, pp.41-46, Non-Dec 2010.

[3] Sukhbir Singh, M. N. Bandyopadhyay, ―Duval Triangle: A Noble Technique for DGA

in Power Transformers‖, International Journal of Electrical and Power Engineering,

vol. 4 no. 3, pp. 193-197, 2010.

[4] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ―Dissolved gas analysis for

power transformers‖, Electrical India, pp. 112-119, March-2012.

[5] Sukhbir Singh, ―A Survey on Testing and Residual Life Assessment of Power

Transformer‖, National Seminar on Power for All by 2012 and Challenges, IEC

Gautam Budh Nagar (Greater Noida) , 11-12th

Jan.2009.

[6] Sukhbir Singh, ―Modern Trends on Power Transformer Fault Diagnosis‖,

Management and Technology Journal, Agra, vol. I, pp. 21-27., Dec. 2008.

[7] Sukhbir Singh‘ ―Comparative Study on Fault Diagnosis on Power Transformers‖,

International Conference on Transformers TRAFOSEM-2008, ITMA, New Delhi, 11-

12th

Nov.2008.

[8] Sukhbir Singh, ―New Trends on Power Transformer Fault Diagnosis‖, National

Conference on Recent Advances in Electrical Engineering RAEE-2008, NIT

Hamirpur (HP), Dec. 26-27, 2008.

Accepted Papers

[1] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ―Review on computer

applications for fault diagnosis on power transformers‖, Journal of Electrical

Engineering, 2011.

[1] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ―Feed-forward BP ANN Duval

triangle method for DGA fault analysis of power transformers‖, IEEE Electrical

Insulation Magazine, 2012.

245

Communicated Papers

[1] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ―New fuzzy logic Duval

triangle techniques for fault diagnosis in power transformers‖, WSEAS Trans. on

Power Systems, 2011.

[2] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ― A review on computational

intelligence for fault diagnosis on power transformers,‖ IEEE Computational

Intelligence Magazine, 2011.

[3] Sukhbir Singh, Dheeraj Joshi, M. N. Bandyopadhyay, ―Feed-forward back-

propagation ANN based Duval triangle method for fault diagnosis in power

transformers‖, International Journal of Electrical Power Engineering, 2011.

dissolved gas analysis for fault diagnosis of power transformers sukhbir singh

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