Abstract— Power transformers are one of the most important

equipment of power system. Fault diagnosis of transformers plays an

active role in power system monitoring and control that ensures a

stable and continuous electrical power supply to consumers. This

project introduces a fuzzy logic which can handle imprecise vague

and uncertainly based fault diagnosis system (FDS) for power

transformers. The system is designed to detect, localize and finally

classify faults. The performance of fault diagnosis system is evaluated

using three phase primary voltages and currents and three phase

secondary voltages and currents and negative sequence components

values. Using the computer program MATLAB, a 500Kv network is

taken as an example. A laboratory simulation transmission line

system in the transmission line laboratory was taken as a practical

example for testing this system; line voltage of this system is 220Kv.

The results proof effectiveness of the system for fault diagnosis, more

than 95%.

Keywords—Power transformer, faults identification and

classification, fuzzy logic.

I. INTRODUCTION

OWER transformer is major component of power system

which has no substitute for its major role. Therefore,

failure of a power transformer may cause a break in power

supply and loss of profits. Hence, it is vital to detect failures in

power transformers as early as possible, in order, to switch

them safely and improve the reliability of power system. The

monitoring and diagnostic techniques of power transformer,

therefore, have attracted considerable attention of many years.

In this work, a transmission system has been simulated with

different types of faults that might be occurred on the primary

and secondary terminals of transforms. This work applied the

obtained results on fuzzy logic model that design for faults

identification and classification.

Calorin, Danial is an academic staff member in Software Engineering

Department – Engineering Faculty – Koya University.

(caroline.yousif@koyauniversity.org)

Dr. Saman. Abdullah is an academic staff member in Software

Engineering Department – Engineering Faculty – Koya University. He is also

the head of Higher Education and Scientific Affair Unit. He is a member in

IEEE since 2010. (sama.mirza@koyauniversity.org)

Ari, Abdurrahman is an academic staff member in Software Engineering

Department – Engineering Faculty – Koya University. Email

( ari.arif@koyauniversity.org)

II. CITED WORKS

In 1993, a work has been proposed by James J. Dukarm to

detect and classify faults of power transformers[1]. The author

was depended on some analytical theory using fuzzy method to

analyze Dissolved – gas for define and classify faults.

Because some faults in transformers are occurred internally, an

work has been achieved by two researchers (Satish and

Santosh) on 1998 to detect the inter fault based on impulses

tests [2]. They were depended on wave analysis techniques,

where responses from have been analyzed to detect internal

faults. New method has been modeled by Yann-chan Huan on

1999. The author has proposed an evolutionary based neural

network to improve models that depend on Dissolved gas

analysis (DGA) [3]. Other researches (Hongzhong at all) have

designed an approach based on IEC three ration methods to

detect external faults. They utilized fuzzy - three ration

method to avoid classical three ratio method [4]. More

researchers are utilized fuzzy logic for fault identification and

classification recently [5, 6]. They showed the ability of fuzzy

technique in detection and classification faults.

III. FUZZY SYSTEM FOR FAULT DETECTION

(INTERNAL FAULTS)

A. System information

Through using of Matlab/Simulink software, a power system

with (500KV) has been simulated. The system has one power

generation with a three phase power transformer (three single

line transformers connected in Y/Yg base), and transmission

lines with loads. Table 1, shows all information about the

simulated system.

As mentioned in Fig. 1, the transmissions line starts at HD500

in the source side and ends at CA500 in receiving side. The

overall length of transmission line is 788 KM. TABLE I

SYSTEM COMPONENTS AND THEIR DETAILS

Feeding

Source

Transformer

Data

Transmission

Line Length

Loads

S=

20

0 M

VA

V=

13

.8 K

V

f= 5

0 H

z

S= 200 MVA,

15.75 ∆/

500YKV

R1=0.0024806Ω,

R2=0.8329Ω,

X1=0.006Ω,

X2=1.67Ω,

Rm=620.16Ω,

X==619.836 Ω

HD

to

NH

= 2

35

Km

NH

to

AS

= 1

83

.6 K

m

AS

to

SA

M=

16

0 K

m

SA

M t

o C

A=

20

9 K

m HD500 =10.32+j103.32

NH500 =198.3+j132.17

AS500= 85.12+j42.56

SA500= 45.44+j40.89

CA500 =157.43+j107.6

Calorin. Danial, Dr. Saman. Abdullah, and Ari. Abdulrahman

A Fuzzy Logic Based Approach for Fault

Classification in Power Transformer System

P

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 2, Issue 2 (2014) ISSN 2320–4028 (Online)

92

Load3

T.L4 T.L3 T.L2 T.L1

HD15.75 HD500 NH500 AS500 SAM500 CA500

Load1 Load2 Load4 Load5

G ∞

Fig. 1 The simulated power system

B. Fault Detection

Faults have been detected based on monitoring the negative

sequence voltage (Va2) and negative sequence current (Ia2) at

the primary side of the transformer, beside the peak value of

current at each phase. The value of (Ia2) current at balanced

state of the system is close to zero; however, its value will be

increase when the system becomes unbalanced. Likewise, the

peak value of current at each phase increases by three folds

when fault occurred at (1%) of total turns, and to five folds

when fault reaches 5%. Table-2 shows a part of records

concerned the relation between the value of negative sequence

of current and voltage with peak values and the percentage of

fault related to turns on phase (A) only. There are same

records for the phases (B) and (C). TABLE II

PART OF NEGATIVE SEQUENCE OF CURRENT AND TYPE OF FAULTS

Va2 Ia2 Peak Current

at phase (A)

Peak Current

at phase (A)

Peak Current

at phase (A)

Faults

0 0 307.2 307.2 307.2 N

1482 354.8 307.2 307.2 307.2 1%

2195 525.6 1198 411.4 830.7 2%

2613 625 1387 488.5 929.6 3%

2886 691 1511 541.9 994.4 4%

3079 737.2 1598 580.6 1040 5%

C. Fuzzy in fault detection

Fig.2, is the fuzzy system that has been designed for each

phases of A, B and C. At each phase, three inputs have been

applied; the first is the value of negative sequence of voltage

which maps to a one of the five membership functions (N,

M.N, M, H, V.H). The range of the Va2 is (0-4000) Volta.

The second input is negative value of current, which maps to

same membership function. However, it has different range (0-

900) Amp. The third value is primary current value, which

maps to same membership function with different range (0-

2000) Amp.

The output of this system has 11 functions which represent

faults over (0-12) ranges as mentioned in the Fig.2. Moreover,

Fig.3 shows how input values over each membership functions

are mapped and how the output could be obtained.

Fig. 2 The Fuzzy based fault detection system

Fig. 3 The membership functions for input and output of fuzzy

system

Through this system, we can define the area that fuzzy

system can work to detect faults and classify them, as shown in

the Fig.5.

Fig. 4 membership function distribution for 2% short-circuit over

primary turn of phase A

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 2, Issue 2 (2014) ISSN 2320–4028 (Online)

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Fig. 5 Area where Fuzzy system can detect and classify

IV. FUZZY SYSTEM FOR FAULT DETECTION

(EXTERNAL FAULTS)

A. Fault Detection

To detect and classify the external faults, this work depends

on negative sequence value of (Va2) and negative sequence

value of (Ia2) and the phase current, as a part of these records

mentioned in Table-3. Fig. 6 shows the signals of current and

voltage how they are normal, and when a fault detected how

the shop is changed. The figure shows three cases, normal, AB

fault, and ABC fault.

Fig. 6 Relation between signals and type of external faults

TABLE III

PART OF NEGATIVE SEQUENCE OF CURRENT AND EXTERNAL FAULTS

Va2 Ia2 Peak Current at

phase (A)

Faults

0 0 307.2 N

325.4 78.08 5031 AG

319.6 84.36 3397 BG

318.3 68.98 3850 CG

6211 1606 4280 AB

6180 1469 307.4 BC

N=Normal, AG=Phase A- Ground Fault, BG=Phase B- to – Phase G Fault,

CG=Phase C- to- Phase G fault, AB= Phase A- Phase B Fault, BC=Phase B –

to – Phase C fault, ….

B. Fuzzy detection based system (External Faults)

The Fig.7 shows the design and graphical user interface for

the fuzzy system that utilized for external fault detection and

classification. The model consists of three inputs (Fig.8); the

first is negative sequence of voltage (Va2) which has the range

(0-7000) over four types of member functions (N, M, H, V.H).

The second input is negative sequence of current (Ia2) which

has a range (0-1800) over six membership functions (N, NN,

N.M, N.H, M, H, V.H). The third input is the current of a

phase which has ranges over (0-6000) with membership

functions (N, M.N, N.H, M, H, V.H). The output of this

system has nine membership functions which represent the

types of faults that this system can detect and classify.

Fig. 7 The design and graphical user interface

Fig. 8 Membership functions of Fuzzy system

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 2, Issue 2 (2014) ISSN 2320–4028 (Online)

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Mapping inputs over the membership functions can be

illustrated in Fig.9, however, the surface three dimensions for

the area that the fuzzy system can detect and classify is

illustrated in Fig. 10.

Fig. 9 Membership function distribution for 2% short-circuit over

primary turn of the phase A

Fig. 10 The three dimensions surface for the fuzzy based detection

and classification system

V. CONCLUSION

This project presented the ability of fuzzy logic system to

detect and classify faults in power transformers. The work

found the fuzzy system can read negative sequence value for

voltage and current with a current on a specific phase to detect

if there is a fault or not. Moreover, it can classify the fault

according to their values.

REFERENCES

[1] Dukarm, J.J. Transformer oil diagnosis using fuzzy logic and

neural networks. in Electrical and Computer Engineering, 1993.

Canadian Conference on. 1993. IEEE.

[2] Pandey, S.K. and L. Satish, Multiresolution signal decomposition:

A new tool for fault detection in power transformers during

impulse tests. Power Delivery, IEEE Transactions on, 1998. 13(4):

p. 1194-1200.

[3] Huang, Y.-C., Artificial Intelligence Approach to Condition

Assessment of Electric Equipment.

[4] Hongzhong Ma ; Dept. of Electr. Eng., H.U., Nanjing ; Zheng Li ;

Ju, P. ; Jingdong Han Diagnosis of Power Transformer Faults

Based On Fuzzy three-ratio method, in The 7th International

Power Engineering Conference, 2005. IPEC 2005.2005, IEEE

Xplore: Singapore. p. 1-456.

[5] Duan, H.D. and X. Yao, Power Transformers Fault Diagnosis

Based on Fuzzy-RBF Neural Network. Advanced Materials

Research, 2013. 614: p. 1303-1306.

[6] Sethuraman, K. and A. Mangaiyarkarasi, Fuzzy Based Fault-

Diagnostic Method in Three Level DC-DC Converter.

International Journal of Innovative Research and Development,

2014. 3(2).

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