condition assessment of transformer by park’s vector and

2013 IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems

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Condition Assessment of Transformer by Park’s Vector and Symmetrical Components to detect

Inter Turn Fault

P. A. Venikar M. S. Ballal B. S. Umre H. M. Suryawanshi Research Scholar Associate Professor Associate Professor Professor

Department of Electrical Engineering Visvesvaraya National Institute of Technology

Nagpur, India

Abstract—Power transformer is a strategically important component of power system due to its location and size. Sudden failure of power transformer has both technical and economical impacts. Studies indicate that inter turn fault is one of the major cause of transformer failure. Detection of presence of such fault at an early stage is important to avoid further damage. Objective of this paper is to analyse the characteristics of current during inter turn fault using Park’s vector and symmetrical components approach. Comparative assessment of Park’s vectors and symmetrical components is also discussed. Use of percentage deviation of vectors from healthy condition is proposed for identification of existence of such fault.

Keywords— d-q vectors; Inter turn fault; Park’s transformation, Power transformer; Symmetrical components

I. INTRODUCTION In modern interconnected power system transformers are

required throughout the system from generation to transmission and distribution. The size varies from few kVA to over hundreds of MVA [1-3], with large repair and replacement cost. In the current restructured electricity market, unplanned outage of transformer can cause substantial loss due to non-availability of supply and loss of revenue. If the failure occurs in service, the impact can be far reaching because of their critical location. Case studies [4] and [5] indicate causes and effects in the form of loss of revenue due to transformer failure. The reason of transformer failure is mainly improper maintenance. The impact of failure is extended outage, costly repairs and replacements and it may also result in potentially serious injury or fatality to the operating personnel [6].

A continuous on-line monitoring strategy can however help in avoiding this loss, as it will determine the status of transformer condition and it can indicate when the maintenance needs to be carried out. In [7], condition monitoring is defined as, a predictive method considering the fact that equipments will have a useful life before maintenance is required. Monitoring is referred as sensor development and data acquisition and collection in [8]. Condition monitoring has advantages such as [9]:

Predicts necessity of maintenance activities to be carried out before occurrence of any failure

Limits the probability of destructive failures, thus improving operator safety and quality of supply

Identifies the reasons for failure and reduces maintenance costs

Provides information on the plant operating life, allowing decisions to be made on either plant refurbishment or plant replacement.

Fig. 1. CIGRE report analysis indicating causes of transformer failure and their relative percentage.

Statistics of CIGRE report in Fig. 1 indicate load tap changer and winding are the most probable locations of transformer failure [10]. If load tap changer is considered as a separate unit then winding failure is the most prominent reason for transformer failure. Inter turn fault is the more probable reason for winding failure. It is extremely important to detect and locate the inter turn fault at an early stage to avoid further catastrophic damage. It primarily arises from insulation deterioration [11]. The deterioration may be caused by combination of factors such as ageing, frequent transformer overloading, mechanical and thermal stress, moisture, high transient voltage stresses and prolonged hanging faults.

The primary objective of this paper is to create an inter turn fault on a transformer and to analyse the effect on primary and secondary voltage and currents. The fault is created on primary side of the winding. Comparative assessment of Park’s vector approach and symmetrical components theory is performed to analyse better applicability of one of these two methods. Use of symmetrical components for transformer analysis is reported in [12], Park’s vector analysis in [13, 14] for transformers


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and induction motor [15-18]. Various graphs such as current component along d-axis vs. time, current component along q-axis vs. time, current component along q-axis vs. current component along d-axis and real part of positive sequence component vs. imaginary part of positive sequence component are plotted for both normal and fault condition. The analysis is performed only for balanced loading conditions. Such techniques with further analysis using wavelet, expert system, artificial intelligence (AI) can be used for on line condition monitoring.

II. SIGNAL PROCESSING BY PARK’S VECTOR AND SYMMETRICAL COMPONENT APPROACH

To simplify the analysis, the three phase quantities are transformed into direct and quadrature axis (d-q axis) quantities using Park’s transformation. Park’s components (Id, Iq) can be obtained for the primary currents (Ir, Iy, Ib) by using following relationship

2 1 13 6 6

1 102 2

rd

yq

b

III

II

(1)

Under ideal conditions the three phase currents lead to following Park’s vectors

6 * *sin2d mI I t (2)

6 * *sin2 2d mI I t

(3)

Where Im is the maximum value of supply phase current (A) and ω is angular frequency (rad/sec). In this paper Park’s vectors are computed using (1).

Symmetrical components have the advantage that any set of unbalanced vectors can be transformed into set of balanced vectors. The transformation equations in matrix form are as follows

0

21

22

1 1 11 13

1

r r

r y

r b

I II a a II Ia a

(4)

Where a=1∠120o

Iy1=a2*Ir1 (5)

Ib1=a*Ir1 (6)

Iy2=a*Ir2 (7)

Ib2=a2*Ir2 (8)

Park’s vectors and symmetrical components for faults on different phases with different loading and fault severity are plotted for both practical and simulation waveforms. Then percentage error between ideal signal and actual signal for given loading condition is calculated. Thus, for given

percentage of loading if error is more than certain limit then it can be said that there exists some fault in the transformer.

III. EXPERIMENTAL SETUP Experimentation is carried out on a 400/110 volt, 2.5

kVA, AN cooled transformer. Three such units are connected in star-star (Y-Y). Fig. 2 and Fig. 3 show actual single unit and connection diagram representation of single unit.

Fig. 2. Single unit of transformer with tappings taken out for creating inter turn fault.

Fig. 3. Winding interconnection of a single unit.

Fig. 4 and Fig. 5 show schematic diagram and laboratory setup. Arrangement for experimentation is as follows

Three phase autotransformer to apply balanced three phase supply.

A variable rheostat (400Ω, 4 A) connected between A1 and A2 to create a fault condition on R-phase (13 turns are shorted). The rheostat serves two purposes, creates a fault and limits fault current to safe value. It can also represent initial stage of inter turn fault. Arcing occurs at a later stage which is beyond the scope of this paper.

Load banks to provide different levels of loading.

Current probe (Tektronix AM 503 amplifier, Tektronix A6312 100 MHz) and voltage probes (LeCroy AP 031 differential probe) are used to observe the waveforms in digital storage oscilloscope (DSO-Tektronix TDS 2014C). Waveforms are stored on a memory chip and used for further analysis in MATLAB®.


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Focus of this paper is to study the effect of inter turn fault on primary side and hence, further analysis is carried out based on fault on primary side only. Different number of tap positions, fault resistance and loading conditions are used to study the behaviour of primary current. In all cases it is observed that as compared to no fault primary current the primary current in case of fault is always on a higher side. Thus, primary side fault reflects in primary side current and does not affect secondary quantities. Fault on primary side can be seen as an auto-transformer action. This observation is shown in Fig. 6.

Fig. 4. Connection diagram.

Fig. 5. Experimentation setup.

IV. PARK’S VECTOR AND SYMMETRICAL COMPONENTS ANALYSIS

To distinguish between a normal and a fault case Park’s vector analysis and symmetrical components is used. For fault on R-phase transformer is loaded to 70 percent, for Y-phase it is loaded to 80 percent and for B-phase it is loaded to 85 percent. In all the cases load is balanced and fault is created on primary side by shorting 13 number of turns (less than 4%). Increase in Y-phase primary current due to fault current can be seen in Fig. 7. Simulation is performed in MATLAB® using multi-winding transformer block. It offers flexibility of providing different number of turns to create inter-turn fault.

Fig. 6. Increase in primary current due to inter turn fault on R phase of primary winding (experimentation waveform).

Fig. 7. Increase in Y-phase primary current due to inter turn fault whereas current in other phases is unchanged.

Symmetrical components and Park’s vectors are plotted in Fig. 8 and Fig. 9 for waveforms shown in Fig.7.

Fig. 8. Symmetrical component analysis for fault on Y-phase

(experimental)

The Park’s transformation and symmetrical components are obtained in MATLAB®. Fig. 8 and Fig. 9 clearly shows the difference between no fault and fault case waveforms of positive sequence components, d-axis and q-axis components for both cases.


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Fig. 9. Deviation of Park’s vector components from no fault components (experimental).

A comparative analysis between Park’s vector components and symmetrical components is performed. Fig. 10(a) indicates primary currents for normal case where no fault is created on any of the phases. Corresponding d-vector versus q-vector plot and real component of Ir1 versus imaginary component of Ir1 are plotted in Fig. 10(b) and Fig.10(c) respectively. Simulation waveforms for same case are shown in Fig. 11(a), Fig. 11(b) and Fig.11(c).Fig. 12(a) indicates increase in primary current of Y-phase for an inter turn fault on that particular phase. It can be seen that fault on Y-phase does not reflect on fault in R-phase or B-phase. Increase in major axis diameter is common for both approaches. It is also seen that percentage change in d-vector versus q-vector plot and real component of Iy1 versus imaginary component of Iy1 plot is same in this case. However Park’s vector provides an additional feature of change in orientation of major axis (Fig. 12 (b) and Fig. 13 (b)).

For fault on R-phase (Fig. 14(a)) simulation results shown in Fig. 14(b), Fig. 14(c) indicate increase in major

axis length for both Park’s vector plot and symmetrical components plot. For fault on B-phase, the Park vector plot indicates change in orientation of major axis along with increase in major axis length. This behavior is similar for fault on Y-phase. However, in both cases the orientation is different which is shown in Fig. 13(b) and Fig. 15(b). Fig.13 (c), Fig.14 (c), Fig.15 (c) indicates there is increase in major axis length with same orientation. However, it can be seen that in addition to increase in major axis length Park’s vector plot provide an additional feature of change in orientation of major axis length.

The plots indicate that percentage deviation from normal condition is similar for both Park’s vector and symmetrical components. This observation is confirmed for different loading conditions and fault severity. However, the orientation of d-component versus q-component is different depending on the faulted phase which is not observed for symmetrical components.This feature of deviation from normal condition and major axis orientation can be used to detect the presence of such a fault. For a particular loading if the deviation exceeds certain tolerance level (considering metering error) then presence of fault can be predicted. Use of symmetrical components involves complex calculations (4) whereas Park’s computation deals with simple mathematical calculations (1). This is useful from digital signal processor (DSP) implementation point of view.

A database can be formed considering different loading conditions, severity of fault, unbalanced load, presence of harmonics, sag, swell, etc. and useful information regarding presence of fault can be extracted. Modern techniques such as cross wavelet analysis, expert system, AI based systems can also be used for pattern recognition and classification. Such a system can be implemented in microcontrollers for on line condition assessment. However, this approach needs to be modified in case of unbalanced loading conditions, constructional differences and harmonics.

Fig.10(a). No fault primary currents (experimental)

Fig. 10(b). No fault d-axis vs. q-axis components plot

(experimental)

Fig. 11(c). No fault real component of Ia1 vs. imaginary component of Ia1 plot (experimental)

Fig.11(a). No fault primary currents (simulation)

Fig. 11(b). No fault d-axis vs. q-axis components plot

(simulation)

Fig. 11(c). No fault real component of Ia1 vs. imaginary component of Ia1 plot (simulation)


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V. CONCLUSION Comparative assessment of Park’s transformation and

symmetrical components to detect inter turn fault through

experimental studies is presented in this paper. Less than 4 percent of turns are shorted to create an inter turn fault. Current in primary winding during inter turn fault is found to be more than that for no fault case for same loading condition. It is also

Fig. 12(a). Increase in Y-phase primary current due to inter turn fault (experimental) Fig. 12(b). Increase in length and change in

orientation of major axis due to inter turn fault (experimental)

Fig. 12(c). Increase in the length of major axis due to inter turn fault (experimental)

Fig. 13(a). Increase in Y-phase primary current due to inter turn fault (simulation)

Fig. 13(b). Increase in length and change in orientation of major axis due to inter turn fault (simulation)

Fig. 13(c). Increase in the length of major axis due to inter turn fault (simulation)

Fig. 14(a). Increase in R-phase primary current due to inter turn fault (simulation)

Fig. 14(b). Increase in the length of major axis due to inter turn fault (simulation)

Fig. 14(c). Increase in the length of major axis due to inter turn fault (simulation)

Fig. 15(a). Increase in B-phase primary current due to inter turn fault (simulation)

Fig.15(b). Increase in length and change in orientation of major axis due to inter turn fault (simulation)

Fig.15(c). Increase in the length of major axis due to inter turn fault (simulation)


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observed that fault in primary winding has no effect on secondary quantities. Park’s vector and symmetrical components plots indicate increase in length of major axis due to inter turn fault. This can be used as a feature to detect inter turn fault. For a given loading if percentage deviation is more than a specific limit then presence of fault can be predicted. Park’s vectors provide additional information about faulted phase based on orientation. Park’s vector also simplifies the analysis and implementation on DSP as compared to symmetrical components. However, in this study load is balanced in all the cases. It is also necessary to consider different factors such as constructional errors, effect of harmonics, metering error and unbalanced loading before deriving final conclusion. These factors will be considered in future experimentation and analysis.

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