characterization of transients and fault diagnosis in transformer by discrete

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),

ISSN 0976 – 6553(Online) Volume 5, Issue 8, August (2014), pp. 21-35 © IAEME

21

CHARACTERIZATION OF TRANSIENTS AND FAULT DIAGNOSIS IN

TRANSFORMER BY DISCRETE WAVELET TRANSFORM AND

ARTIFICIAL NEURAL NETWORK

M. Mujtahid Ansari1, Nilesh S. Mahajan

2, Dr. M A Beg

3

1, 2

Department of Electrical Engineering, SSBT’s COET, Bambhori. (MS) India 3Department of Electrical Engineering, Al-Qassim University, Buraidah, Kingdom of Saudi Arabia

ABSTRACT

The transients to which the transformer is mainly subjected are impact of high voltage and

high frequency wave arising from various causes. Switching magnetization and inter-turn faults are

also responsible for transients phenomena. To characterize and discriminate the transient arising

from magnetization and inter-turn faults are presented here. This characterization will give value

added information for improving protection algorithm. The detection method can provide

information to predict fault ahead in time so as that necessary corrective actions are taken to prevent

outages and reduce down time. The data is taken from different test results like normal

(magnetization) and abnormal (inter-turn fault) in this work; Discrete Wavelet Transform concept is

used. Feature extraction and method of discrimination between transformer magnetization and fault

current is derived by Discrete Wavelet Transform (DWT) Tests are performed on 2KVA,

230/230Volt custom built single phase transformer. The results are found using Discrete and

conclusion presented. The spectral energies of the wavelet components are calculated and then

employed to train Artificial Neural Network (ANN) to discriminate an internal fault from the

magnetizing inrush current. The results presented clearly show that the proposed technique can

accurately discriminate between an internal fault and a magnetizing inrush current in transformer

protection.

Keywords: Inrush Current, Internal Fault, Transients, Second Harmonic Component in Transformer,

Wavelet Transform and Artificial Neural Network.

I. INTRODUCTION

To avoid the needless trip by magnetizing inrush current, the second harmonic component is

commonly used for blocking differential relay in power transformers. The major drawback of the

INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &

TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 8, August (2014), pp. 21-35

© IAEME: www.iaeme.com/IJEET.asp Journal Impact Factor (2014): 6.8310 (Calculated by GISI) www.jifactor.com

IJEET

© I A E M E



22

differential protection of power transformer is the possibility for false tripping caused by the

magnetizing inrush current during transformer energization. [5] In this situation, the second

harmonic component present in the inrush current is used as a discrimination factor between fault

and inrush currents. In general, the major sources of harmonics in the inrush currents are

nonlinearities of transformer core; saturation of current transformers; core residual magnetization;

and switching instant. This work proposes new wavelet-based and artificial neural network methods

to identify inrush current and to distinguish it from inter-urn faults. [1-2]

Recently, Artificial Neural Network (ANN) techniques have been applied to power

transformer protection to distinguish internal faults from magnetizing inrush currents [6]. The main

advantage of the ANN method over the conventional method is the non algorithmic parallel

distributed architecture for information processing. In references [5] and [6], neural network based

schemes for protection of a single phase transformer have been investigated. However, the ANNs in

these existing studies are specific to particular transformer systems, and would have to be retrained

again for other systems. Moreover, the employed feature extraction techniques are based on time or

frequency domain signals, and not both time and frequency features of the signal; this is very

important for accurately distinguishing between an internal fault and inrush current.

II. NEED OF FREQUENCY INFORMATION

Often times, the information that cannot be readily seen in the time-domain can be seen in the

frequency domain like ECG signal (Electro Cardio Graph, graphical recording of heart's electrical

activity). The typical shape of a healthy ECG signal is well known to cardiologists. Any significant

deviation from that shape is usually considered to be a symptom of a pathological condition. This, of

course, is only one simple example why frequency content might be useful. Today Fourier

transforms are used in many different areas including all branches of engineering. Although FT is

probably the most popular transform being used (especially in electrical engineering), it is not the

only one. There are many other transforms that are used quite often by engineers and

mathematicians. Hilbert transform, short-time Fourier transform (more about this later), Wigner

distributions, the Radon Transform, and of course our featured transformation , the wavelet

transform, constitute only a small portion of a huge list of transforms that are available at engineer's

and mathematician's disposal. Every transformation technique has its own area of application, with

advantages and disadvantages, and the wavelet transform (WT) is no exception. For a better

understanding of the need for the WT let's look at the FT more closely. FT (as well as WT) is a

reversible transform, that is, it allows going back and forwarding between the raw and processed

(transformed) signals. However, only either of them is available at any given time. That is, no

frequency information is available in the time-domain signal, and no time information is available in

the Fourier transformed signal. The natural question that comes to mind is that is it necessary to have

both the time and the frequency information at the same time? The particular application and the

nature of the signal in hand. Over the years, various incipient fault detection techniques, such as

dissolved gas analysis and partial discharge analysis have been successfully applied to large power

transformer fault diagnosis. Since these techniques have high-cost and some are offline, a low-cost,

online internal fault detection technique for transformers using terminal measurements would be very

useful. [1-3]

A powerful method based on signal analysis should be used in monitoring. This method

should discriminate between normal and abnormal operating cases that occur transformers such as

internal faults, magnetizing inrush. There have been several methods, based on time domain

techniques, frequency domain techniques or time-frequency domain techniques. In previous studies,

researchers have used Fourier transform (FT) or windowed-Fourier transform. In recent studies,

wavelet transform based methods have been used for analysis of characteristics of terminal currents



23

and voltages .Traditional Fourier analysis, which deals with periodic signals and has been the main

frequency-domain analysis tool in many applications, fails in transient processes such as

magnetizing inrush and internal faults. The wavelet transform (WT), on the other hand, can be useful

in analyzing the transient phenomena associated with the transformer faults. Since the FT gives only

frequency information of a signal, time information is lost. Therefore, one technique known as

windowed FT or short-time FT (STFT) has been developed. However, the STFT has the limitation of

a fixed window width. So it does not provide good resolution in both time on other hand, WT

provide great resolution in time for high frequency component of signal and great resolution in

frequency for low frequency components of a signal. In a sense, wavelets have a window that

automatically adjusts to give the appropriate resolution. [2-3].

III. WAVELET APPLICATION

In recent years, researchers in applied mathematics and signal processing have developed

powerful wavelet techniques for the multi scale representation and analysis of Signals These new

methods differ from the traditional Fourier techniques Wavelets localize the information in the time-

frequency plane; in particular, they are capable of trading one type of resolution for another, which

makes them especially suitable for the analysis of non-stationary signals. One important area of

application where these properties have been found to be relevant is power engineering. Due to the

wide variety of signals and problems encountered in power engineering, there are various

applications of wavelet transform. These range from the analysis of the power quality disturbance

signals to, very recently, power system relaying and protection. The main difficulty in dealing with

power engineering phenomena is the extreme variability of the signals and the necessity to operate

on a case by case basis. Another important aspect of power disturbance signals is the fact that the

information of interest is often a combination of features that are well localized temporally or

spatially (e.g., transients in power systems). This requires the use of analysis methods sufficiently

which are versatile to handle signals in terms of their time-frequency localization. Our discussion is

organized into two main parts: (1) a discussion of the main properties of WT and their particular

relevance to power engineering problems and (2) a critical review of power engineering applications.

In Section II, we start by examining the properties of WT that are most relevant to power engineering

problems. We consider the primary power engineering applications, provide the reader with the

relevant background information, and review recent wavelet developments in these areas.

Time-Frequency Localization Wavelets are families of functions generated from one single function, called an analyzing

wavelet or mother wavelet, by means of scaling and translating operations. Some mother wavelets

are shown in Fig.1. The difference between these wavelets is mainly due to the different lengths of

filters that define the wavelet and scaling functions. Wavelets must be oscillatory, must decay

quickly to zero (can only be non-zero for a short period), and must integrate to zero. The scaling

operation is nothing more than performing “stretching” and “compressing” operations on the mother

wavelet, which in turn can be used to obtain the different frequency information of the function to be

analyzed. The compressed version is used to satisfy the high frequency needs, and the dilated version

is used to meet low frequency requirements. Then, the translated version is used to obtain the time

information of the function to be analyzed. In this way, a family of scaled and translated wavelets is

created and serves as the base, the building blocks, for representing the function to be analyzed. The

scaled (dilated) and translated (shifted) versions of the Daubechies mother wavelet are shown in

Fig.2. Daubechies wavelets belong to a special class of mother wavelets and actually are used most

often for detection, localization, identification and classification of power disturbance.



24

Fig.1: Four mother wavelets often used in wavelet analysis

Fig.2: Scaled and translated version of D4 wavelet

IV. EXPERIMENTATION AND DATA COLLECTION

The setup for experiments has a custom built 230V/230V, 2KVA, 50Hz, single-phase

transformer with externally accessible taps on both primary and secondary to introduce faults. The

primary winding and secondary winding has 272 turns respectively. The load on the secondary

comprises of static and rotating elements.

Data acquisition card by Tektronix Instruments was used to capture the voltages and current

signals. The Tektronix DSO TPS2014B, with 100MHz bandwidth and adjustable sampling rate



25

1GHz is used to capture the currents and voltages. The Tektronix current probes of rating 100mV/A,

input range of 0 to 70Amps AC RMS, 100A peak and frequency range 0 to 100Khz are used. These

signals were recorded at a sample rate of 10,000 samples/sec.

Different cases of inter turn short circuit are staged, considering the effect of number of turns

shorted on primary and secondary on load condition. Experimental set up is as shown in fig .3 and

fig.4 The current and voltage signals were captured for inrush and faulted condition, The captured

data are stored in excel sheet with the notations Vp-Primary voltage, Ip-Primary current ,Vs-

Secondary voltage, Is- Secondary current, Ts- Sampling time and FEQ-frequency of supply voltage

at captured instant

Fig.3: Experimental set up

Procedure for data collection

1. The magnetization current is captured at primary side.

2. Inter-turn faults are done on primary and secondary winding through contractor under load

condition.

3. The difference of primary and secondary is done sample by sample. The fifth channel is set in

Math function which directly gives differential current.

4. Current transformer and Voltage transformer are used to capture the current and voltage, The

analog signals are sampled at rate of 10000sample/sec. by Tektronix Digital Oscilloscope

(DSO).

The data is stored in excel sheets using Data Acquisition Card by Tektronix DSO.

V. WAVELET ANALYSIS

At the first stage an original signal is divided in to two halves of the frequency bandwidth,

and sent to both Low Pass Filter (LPF) and High Pass Filter (HPF). The coefficients of filter pairs are

associated with the selection of mother wavelet, the Daubechies Db-4type wavelet is used as mother

wavelet. Then the output of LPF is further cut in half of the frequency bandwidth and then sent to the

second stage, this procedure is repeated until the signal is decomposed to a pre-defined certain level.



26

If the original signal were being sampled at Fs Hz, the highest frequency that the signal could

contain, from Nyquist’s theorem, would be Fs/2 Hz. This frequency would be seen at the output of

the high pass filter, which is the first detail 1; similarly, the band of frequencies between Fs/4 and

Fs/8 would be captured in detail 2, and so on. The sampling frequency is taken to be 10 kHz and

Table I shows the frequency levels of the wavelet function coefficients.

Fig.4: Photo of practical setup

Fig.5: Implementation of DWT

Table I: Frequency levels of Wavelet Functions Coefficients

Decomposition

Level

Frequency

Components, Hz

D1 5000-2500

D2 2500-1250

D3 1250-625

D4 625-312.5

D5 312.5-156.25

A5 0-156.25



27

The waveforms of inrush and fault along with decomposition levels are shown.

Fig. 6: Wave form and decomposition Fig 7: Wave form and decomposition

levels of Inrush differential current levels of Primary fault differential current

Fig 8 (a) Wave form and decomposition levels of magnetization inrush differential Current along

original signal

Fig 8 (b) Wave form and decomposition levels of primary fault differential Current along original

signal

From visual inspection of fig. 6 and fig.7 characterize the transient and discriminate between

magnetization inrush and interturn fault. In these two figures d1 and d2 are nearly similar and

discrimination is difficult. By keen observation at decomposition level d3 and d4 of figure 6, the

wavelet coefficients are corresponds to magnetization peek. Whereas in fig 7, the large wavelet

coefficients for the decomposition level d3 to d5 appears at the instant of swiching and attenuates



28

with the length. Fig 8(a) and fig 8(b) shows the each decomposition level along with original signal

for readily justification of above lines.

Above observations are no longer valid because these decomposition levels signature are

changing with instant of switching as well as instant of fault. To arive some results, the energies of

d3, d4, and d5 decomposed levels for one cycle are calculated and ANN is used for classifications of

transients. Since the d1 and d2 decomposition level are very much mixed to each others, hence d1

and d2 levels are not considered.

Flow Chart for ANN

Fig 9: Flow chart

Description of Flow Chart

1. Captured primary and secondary current by Data acquisition card and Tektronix Instruments.

2. Calculate the differential current Id= Ip- Is

3. If the differential current value is less than threshold value, then take next samples otherwise

proceed with that vale.



29

4. Calculate DWT of differential current whose value is above threshold value.

5. Obtain the energies of decomposed levels from d3 to d5.

6. The energy of decomposed levels d3 to d5 is given to ANN as input data to discriminate the

fault and inrush that is healthy condition.

7. Energies are calculated by formula � � ∑ ��

8. If ANN out put is discriminated as fault, then issue trip signal, otherwise proceed further.

Neural Network as a classifier Because of inherent capability of ANN to classify even though the data are non linearly

separable. Here the data are not classified by other method hence ANN, Multilayer Perceptron

(MLP) Neural Network is trained very systematic manner for classification. Energies of decomposed

level D3, D4, and D5 for one cycle were used as a input to the networks. The cluster diagram is

shown in Data fig. 10 It shows that this is non linearly separable data. For generalization the

randomized data is fed to the network and is trained for different hidden layers. Various training

methods are used for training the network and average minimum MSE on training and testing data is

measured. It is observed that MLP with single hidden layer gives better performance. The number of

Processing Elements (PEs) in the hidden layer is varied. The network is trained and minimum MSE

is obtained.

For all training methods it is assumed that learning rate LR=0.8, momentum MM=0.6, data

used for training purpose TR= 50 %, data used for cross validation CV = 20% and data used for

testing purpose TS= 30%. With these assumptions the variation of average mse and percent accuracy

for both Inrush and fault with respect to number of processing elements in the hidden layer is

obtained. Fig 7.9(a) Shows Variation of average minimum MSE with respect to number of

processing elements in the hidden layer, for one of the training method ‘ traincgf ’ of Conjugate

Gradient.

Fig 10: Cluster diagram, showing mixed energies of d3 to d5 levels.

Conjugate Gradient Method

METHOD: ‘ traincgf ’ (i) MSE Vs Number of PE’S



30

Fig 11 (a): Variation of average minimum MSE with number of processing elements in the

hidden layer

Also average classification accuracy with respect number of processing elements in the

hidden layer, for the same method is obtained and plotted in fig 11 (b).(ii) % ACCURACY Vs

Number of PE’S.

Fig 11 (b): Variation of % Accuracy with number of processing elements in the hidden layer

It is observed from fig 7.9(a) and fig 7.9 (b) that, for this training method eleven number of

processing elements in the hidden are required to get minimum MSE(0.0218) and maximum percent

accuracy for Inrush ( 95% ) ,for fault ( 99% ).Similarly Variation of average minimum MSE and

Variation of percent accuracy with respect to number of processing elements in the hidden layer for

another training method ‘traincgp’ of Conjugate Gradient (CG) obtained, as shown in fig 11 (a) and

fig 11 (b).



31

VI. COMPARISON OF ALL METHOD

Variation of % Accuracy for all training methods with particular number of processing

elements in the hidden layer are plotted from fig 12 to fig 14

Fig 12: Variation of % Accuracy with 13 number of processing elements in the hidden layer




32


VII. COMPARISON of Analysis

Table II: Comparison of MSE for Different Methods

PE'S

MSE FOR DIFFERENT METHODS

traincgf traincgp traincgb trainscg

traincgf

(QN) trainmoss trainlm

1 0.092 0.121 0.115 0.087 0.077 0.0042 0.70

2 0.072 0.244 0.096 0.025 0.112 0.0884 0.035

3 0.073 0.064 0.058 0.033 0.061 0.0938 0.003

4 0.118 0.087 0.044 0.020 0.047 0.0628 0.012

5 0.061 0.021 0.032 0.019 0.019 0.029 0.17

6 0.074 0.040 0.022 0.004 0.091 0.0229 0.006

7 0.094 0.054 0.026 0.050 0.043 0.0464 0.033

8 0.042 0.014 0.032 0.010 0.025 0.0217 0.006

9 0.091 0.03 0.014 0.030 0.022 0.0407 0.055

10 0.155 0.032 0.009 0.006 0.033 0.0396 0.001

11 0.021 0.026 0.031 0.036 0.037 0.0272 0.003

12 0.093 0.010 0.005 0.003 0.024 0.0391 0.0109

13 0.132 0.012 0.022 0.043 0.022 0.0336 0.0007

14 0.021 0.054 0.041 0.027 0.008 0.0147 0.021

15 0.039 0.023 0.029 0.013 0.050 0.0135 0.0001



33

Table III: Comparison of PE Vs Methods for % Accuracy

PE’S

PERCENT ACCURACY OF INRUSH AND FAULT FOR DIFFERENT

METHODS

traincgf traincgp traincgb Trainscg traincgf(

QN)

trainoss trainlm

In F In F In F In F In F In F In F

1 75 96 68 95 71 99 75 92 65 100 80 93 71 99

2 70 100 88 66 66 100 95 99 65 100 70 95 95 98

3 60 99 66 100 90 96 95 99 65 100 70 100 96 99

4 70 88 71 100 88 100 91 99 88 98 90 98 93 99

5 63 100 93 99 93 99 95 98 95 99 91 99 93 99

6 75 97 88 96 93 99 95 99 80 97 93 99 95 100

7 75 97 73 97 95 95 81 100 80 98 95 95 93 99

8 90 98 95 99 95 99 93 99 91 97 93 99 90 99

9 78 75 95 97 96 98 96 98 90 100 80 100 95 99

10 60 100 95 99 98 99 93 99 88 99 88 96 95 99

11 95 99 90 97 91 98 90 97 70 98 95 99 93 98

12 66 98 95 99 93 97 97 99 95 99 93 99 95 99

13 95 97 95 99 95 99 93 99 95 95 95 99 93 99

14 95 96 93 96 95 99 95 98 90 99 93 99 96 98

15 95 98 95 99 95 99 93 99 86 97 93 99 100 100

VIII. DISCUSSIONS ON RESULTS

It is observed from graphs, for every training method the number of Processing element (PE)

in the hidden layer are required to get minimum value of Mean Square Error(MSE) corresponds to

maximum percentage accuracy for discriminating the magnetization inrush and inter-turn fault.

For each method Learning Rule(LR), Momentum(MM),Training data(TR),Cross Validation

data(CV) and Testing data(TS) are kept same. The values are LR= 0.8, MM = 0.6, TR = 50%, CV

=20% and TS = 30%. This strategy is applied to all method mentioned in case study. This strategy is

applied to Conjugate Gradient methods like Traincgf, Traincgp, Traincgb and Traincg. It is found

that up to 15 number of processing element none of method is able to characterize the desired result.

Same exercise is done for Quasi Newton method .The two methods of Quasi Newton such as

Traincgf and Trainnos, are not capable to discriminate the magnetization inrush and fault current

correctly. It is found that by Levenbreg – Marquardt Trainlm method gives 100% classification with

fifteen numbers of neurons or processing element in hidden layer for discrimination and characterize

the magnetization inrush and fault current.

The above discussions become clearer by table no.2 which shows the comparison of MSE for

different methods. It shows that for trainlm method with LR=0.8, MM=0.6, TR= 50 %, CV = 20%

and TS= 30% the MSE is minimum among all other methods for fifteen number of processing

element. The minimum value of MSE obtained is 0.0001.



34

Similarly table no. 3 shows the comparison of number of Processing Elements (PE) verses

methods for percentage accuracy. It also shows that that for trainlm method with LR=0.8, MM=0.6,

TR= 50 %, CV = 20% and TS= 30% the percent accuracy with respect to 15 number of processing

elements is 100%.

It is found that the accuracy of train network for correctly classifying the transient due to

magnetization inrush and fault is 93%.

IX. CONCLUSION

This paper discussed efforts to characterize transients for transformers, resulting from various

types of faults. The experiments were conducted on a single-phase transformer model. The data were

obtained from experiments for several cases related to the transformer operation such as magnetizing

inrush, external system short circuits, internal short circuits. The data were analyzed using discrete

wavelet transforms (DWTs). Same exercise is done for Quasi Newton method .The two methods of

Quasi Newton such as Traincgf and Trainnos, are not capable to discriminate the magnetization

inrush and fault current correctly. It is found that by Levenbreg – Marquardt Trainlm method gives

100% classification with fifteen numbers of neurons or processing element in hidden layer for

discrimination and characterize the magnetization inrush and fault current. The characteristics of the

cases and differences between cases are presented. The results show great potential for using this

method for predictive maintenance and maintaining reliability of transformers. Future work will

investigate using characteristics of fault data with an intelligent method such as neural networks for a

discrimination process and life estimation of transformer.

X. REFERENCES

[1] M Mujtahid Ansari, S R Paraskar and Dr G M Dhole, “Characterization of Transients and

Fault Diagnosis in Transformers Using Discrete Wavelet Transform” International Journal of

Electrical Engineering and Technology (IJEET), Vol. 4, Issue 5, September-October 2013.

Page 87-95.

[2] M Mujtahid Ansari, Patil B P and Dr G M Dhole, “Application of Signal Analysis for Fault

Diagnosis in Transformer by Discrete Wavelet Transform” International Journal of

Engineering Research & Technology (IJERT), Vol. 2 Issue 2, February- 2013. Page 648.

[3] Karen L. Butler-Purry and Mustafa Bagriyanik.” Characterization of Transients in

Transformers Using Discrete Wavelet Transforms” IEEE Transactions on Power system,

Vol. 18, No. 2, May 2003. Page 648.

[4] Jawad Faiz, and S. Lotfi-Fard,” A Novel Wavelet-Based Algorithm for Discrimination of

Internal Faults From Magnetizing Inrush Currents in Power Transformers” IEEE

Transactions on Power Delivery, Vol. 21, No. 4, October 2006 Page 1989.

[5] Omer A S Youssef, A Wavelet Base Technique for Discrimination between fault and

magnetization inrush current in transformer”, IEEE Transactions on Power Delivery, Vol. 18

No.1 January2003. Page 170.

[6] S A Saleh & M A Rahman.”, Modeling and protection of three phase transformer using

wavelet packet transform,” IEEE Transactions on Power Delivery, Vol 20 No.2 April 2005

Page 1273.

[7] Peilin Mao and Raj K Aggarwal ,” A noval approach to classification of the transient

phenomena in power transformers using combine Wavelet Transform and Neural Network,”

IEEE Transactions on Power Delivery , Vol 16 No.4 October 2001 Page 654.



35

[8] Sami G Abdulsalam,” Analitical study of transformer inrush current transient and it’s

application,” International conference on power transient in Montreal, Canada on June

19-23, 2005 paper PST05-140.

[9] Jialong Wang and Randy Hamilton, Analysis of transformer inrush current and comparison

of harmonic restrain method in transformer protection, Basler Electric Company,

978-1-4244-4108( c ), 2008 IEEE.

[10] H. L. Willis, G. V. Welch, and R. R. Schrieber, Aging Power Delivery Infrastructures. New

York: Marcel Dekker, 2001.

[11] H. Wang and K. L. Butler, “Modeling transformers with internal incipient faults,” IEEE

Trans. Power Delivery, vol. 17, pp. 500–509, Apr. 2002.

[12] A.V.Padmaja and V. Usha Reddy, “Application of Wavelet Transform for Monitoring Short

Duration Disturbances in Distribution Systems” International Journal of Electrical

Engineering and Technology (IJEET), Vol. 3, Issue 1, January-June 2012. Page 112 - 122.

AUTHOR’S DETAIL

M. Mujtahid Ansari received his B.E.( Electrical Engg) and M.E.(Electrical Power

System) from the S.G.B.University, Amravati, India in year 1996 and 2009

respectively . He is member of IE, ISTE and IACSIT. He has joined SSBT’s College

of Engineering and Technology, Jalgaon in the year 2000 where he is working as

Assistant Professor in Electrical Engineering Department. He has published three

papers in international journal and one text book. He is chairman Board of studies in

Electrical Engineering and Instrumentation Engineering under the faculty of Engineering and

Technology, North Maharashtra University, Jalgaon (MS) India.

Nilesh S. Mahajan had completed his M. E. in Electrical Power Systems from

Government College of Engineering, Aurangabad. He is working as an Assistant

Professor in the Department of Electrical Engineering in SSBT’s College of

Engineering & Technology, Bambhori, Jalgaon. He has published two papers in

international journal.

Dr. M. A. Beg received his B.E. and M.E. from the S.G.B.Amravati University

,Amravati, India in year 1992 and 2001 respectively in Electrical Power system

Engineering and completed his Ph.D. from the S.G.B.University, Amravati, in Power

Quality. In the year 1990, he joined S.S.G.M. College of Engg. Shegaon, where he

had worked Assistant Professor in Electrical Engg. He also served as head and

academic dean in the same college. Presently he is working as professor in department

of Electrical Engineering, Al-Qassim University, Buraidah, Kingdom of Saudi Arabia, His present

research interest includes power quality monitoring and signal processing technique applications in

power systems. He is member of IACSIT and ISTE. He has published research papers in different

International and National Journals and Conference Proceedings He is reviewer of International

Journal “Electrical Power Components & System (EPCS), Taylor & Fransis Group Publication,

England, International Journal EPSR (Elsevier).