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PARAMETER IDENTIFICATION FOR FAULT DETECTION OF POWER
TRANSFORMER USING ARTIFICIAL NEURAL NETWORK
RUZAINI BINTI ROSLI
UNIVERSITI TEKNOLOGI MALAYSIA
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PARAMETER IDENTIFICATION FOR FAULT DETECTION OF POWER
TRANSFORMER USING ARTIFICIAL NEURAL NETWORK
RUZAINI BINTI ROSLI
A project report submitted in partial fulfilment of the
requirements for the award of the degree of
Master of Engineering (Electrical – Power)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
JUNE 2015
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Dedicated to my beloved parents
Rosli bin Abdul Hamid & Wan Mazlina binti Wan Mahmood
Siblings
Rashidah binti Rosli
Muhammad Afiq bin Rosli
and
All my friends in MEP programme
for their support and encouragement
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ACKNOWLEDGEMENT
First and foremost, “ Syukur Alhamdulillah” to Allah, the Most Gracious and
Most Merciful for ensuring myself to be healthy to carry out my study and to
complete this project.
Secondly, I would like to express my warmest gratitude to my supportive
supervisor, Prof. Ir. Dr. Abdullah Asuhaimi bin Mohd Zin who has provided
immeasurable support and guidance toward the completion of my research project.
His remarkable ideas and suggestions will be much appreciated in the long run of my
career.
My sincere appreciation also goes to my family especially my parents Rosli
bin Abdul Hamid and Wan Mazlina binti Wan Mahmood who has been so tolerant
and supportive in all years either morally or financially. Thanks for their continuous
encouragement, love and emotional supports that they had given to me all this while.
I also would like to gratefully thank to all my lecturer and all my friends who
had given me helps technically and mentally throughout my journey in completing
my project. Thanks a lot from the bottom of my heart. I wish you all the best in life
and hope that our friendship will never last forever.
Thank You.
Wassalam
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ABSTRACT
Fault diagnosis is a challenging problem because there are numerous fault
situations that can possibly occur to an electrical transformer. There are a lot of
previous works done by researchers on fault diagnosis in power transformer but all of
them used data from Dissolved Gas Analysis (DGA) as input for detection. This
study will focus on parameter identification that is electrical measurement, which is
voltage and current for fault detection due to several limitations of data from DGA
that can lead to wrong diagnosis of fault in power transformer. The transformer that
been used in this power system model is 132/20 kV with 250 MVA rating. The
simulation of nine types of possible fault has been done by MATLAB R2013a
Simulink software. To recognize the pattern of fault data, ANN was chosen because
of it was easy to apply in power system network and it will work as pattern classifier
with the ability to identify fault types accurately. The ANN programming has been
done by ANN Pattern Recognition Tool that also in MATLAB R2013a software. It is
found that the fault of power transformer can be detected by measuring electrical
parameter such as voltage and current and with ANN, detection and classification of
fault can be done to diagnose fault in power transformer. After the fault data had
been trained for a few times, ANN will learn how to classify it accurately and then it
is able to properly resolve new situations which are different from those fault data
presented in the learning process.
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ABSTRAK
Diagnosis kerosakan adalah masalah yang mencabar kerana terdapat
banyak situasi kesalahan yang mungkin boleh berlaku kepada pengubah kuasa
elektrik. Terdapat banyak kerja-kerja sebelum ini dilakukan oleh penyelidik di
diagnosis kerosakan dalam pengubah kuasa tetapi semua daripada mereka
menggunakan data daripada Analisis Gas terlarut (DGA) sebagai input untuk
pengesanan. Kajian ini akan memberi tumpuan kepada mengenal pasti parameter
pengukuran elektrik, iaitu voltan dan arus untuk mengesan kerosakan kerana
beberapa batasan data dari DGA yang boleh membawa kepada kesilapan dalam
diagnosis kerosakan pengubah kuasa. Pengubah kuasa yang digunakan dalam
sistem model ini ialah pengubah kuasa 132/20 kV dengan 250 MVA. Simulasi
sembilan jenis kerosakan yang mungkin berlaku telah dibuat dengan
menggunakan perisian MATLAB Simulink R2013a. Untuk mengenali corak data
kerosakan, ANN telah dipilih kerana ia adalah mudah untuk memohon dalam
rangkaian sistem kuasa dan ia akan bekerja sebagai corak pengelas dengan
keupayaan untuk mengenal pasti jenis kerosakan dengan tepat. Pengaturcaraan
ANN telah dilakukan oleh Alat Pengiktirafan Corak yang juga dalam MATLAB
perisian R2013a. Didapati bahawa kerosakan pada pengubah kuasa boleh dikesan
dengan mengukur parameter elektrik seperti voltan dan arus dan dengan ANN,
pengesanan dan pengkelasan kerosakan boleh dilakukan untuk diagnosis
kerosakan dalam pengubah kuasa. Selepas data kerosakan telah dilatih untuk
beberapa kali, ANN akan belajar untuk mengelaskannya dengan tepat dan
kemudian ia mampu untuk menyelesaikan situasi baru yang berbeza daripada data
kerosakan dalam proses pembelajaran.
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xi
LIST OF ABBREVIATIONS xiii
LIST OF APPENDICES xiv
1 INTRODUCTION 1
1.1 Backgroud 1
1.2 Problem Statements 3
1.3 Objectives 4
1.4 Scope of Study 4
1.5 Methodology 5
1.6 Structure of Thesis 6
2 LITERATURE REVIEW 7
2.1 Introduction 7
2.2 Power Transformer Fault Diagnosis Method 7
2.2.1 Artificial Neural Network 8
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2.2.2 Support Vector Machine 11
2.2.3 Fault Tree Analysis 13
2.2.4 Fuzzy 14
2.2.5 Hidden Markov Model 16
2.3 Summary 18
3 ARTIFICIAL NEURAL NETWORK 19
3.1 Introduction 19
3.2 Background of ANN 21
3.3 Application of ANN 23
3.4 Learning Process of ANN 25
3.4.1 Supervised Learning 25
3.4.2 Unsupervised learning 26
3.5 Summary 27
4 METHODOLOGY 28
4.1 Research Methodology 28
4.2 MATLAB 30
4.3 Circuit Components 31
4.3.1 Three-Phase Source 31
4.3.2 Three-Phase Series RLC Branch 32
4.3.3 Three-Phase Transformer 33
4.3.4 Three-Phase Fault 33
4.3.5 Three-Phase Parallel RLC Load 34
4.3.6 Powergui 35
4.3.7 Three-Phase V-I Measurement 36
4.3.8 Scope 37
4.4 Simulation Model 38
4.4.1 Fault Simulation 39
4.4.2 Data for Training ANN 39
4.5 Artificial Neural Network Programming 41
4.5.1 Neural Network Programming 41
4.5.2 Neural Network Pattern Recognition 42
4.5.3 Data Selection 43
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4.5.4 Validation and Test Data 44
4.5.5 Network Architecture 45
4.5.6 Train Network 46
4.5.7 Evaluate Network 47
4.5.8 Save Results 48
4.6 Summary 49
5 RESULTS AND DISCUSSION 50
5.1 Introduction 50
5.2 ANN Analysis 51
5.2.1 ANN Performance 51
5.2.2 ANN Training State 52
5.2.3 ANN Error Histogram 53
5.2.4 ANN Confusion 54
5.2.5 ANN Receiver Operating Characteristic 55
5.3 Performing Additional Test for ANN 56
5.3.1 Data Selection for ANN Additional
Testing 57
5.3.2 Result of ANN Additional Testing 57
5.4 Summary 61
6 CONCLUSION AND FUTURE WORKS 62
6.1 Conclusion 62
6.2 Future Work 63
REFERENCES 64
Appendix A-F 68-93
x
LIST OF TABLES
TABLE NO. TITLE PAGE
3.1 Examples of areas of application using ANN 24
4.1 Current and Voltage Values at Steady State 39
4.2 Current and Voltage data at A-G Fault 40
5.1 Data for Additional Testing 57
xi
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1 Types of fault in power transformer 2
2.1 Structure of neural network 9
2.2 The optimal separating hyperplane 11
2.3 Fuzzy inference system 14
3.1 The biological neurons of human brain 20
3.2 General idea of ANN 22
3.3 Example of ANN fault classifier structure 23
3.4 Configuration of ANN supervised learning 26
3.5 Configuration of ANN unsupervised learning 27
4.1 Work process flowchart 29
4.2 Three-Phase Source block 32
4.3 Three-Phase Series RLC branch block 32
4.4 Three-Phase Transformer (Two Windings) block 33
4.5 Three-Phase Fault block 34
4.6 Three-Phase Parallel RLC Load block 34
4.7 The Powergui block 35
4.8 Powergui’s functions 36
4.9 Three-Phase V-I Measurement block 37
4.10 Scope block 37
4.11 Circuit design by using MATLAB R2013a Simulink 38
4.12 Neural Network Start (nnstart) 42
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4.13 Pattern Recognition Tool 43
4.14 Data selection for Pattern Recognition Tool (nprtool) 44
4.15 Validation and Test Data for Neural Network
Pattern Recognition Tool 45
4.16 Network Architecture of Neural Network
Pattern Recognition Tool 46
4.17 Train Network of Neural Network
Pattern Recognition Tool 47
4.18 Evaluate Network of Neural Network
Pattern Recognition Tool 48
4.19 Save Result of Neural Network
Pattern Recognition Tool 49
5.1 Mean Squared Error (mse) versus Epoch graph 52
5.2 ANN Training State 53
5.3 ANN Error Histogram 54
5.4 ANN Confusion 55
5.5 ANN Receiver Operating Characteristic 56
5.6 ANN Confusion Matrix for Additional Testing 58
xiii
LIST OF ABBREVIATIONS
ANN - Artificial Neural Network
SVM - Support Machine Vector
IEC - International Electrotechnical Commision
GA - Genetic Algorithm
FTA - Fault Tree Analysis
HMM - Hidden Markov Model
DGA - Dissolved Gas Analysis
OLTC - On Load Tap Changing
ELM - Extreme Learning Machine
DFTA - Dynamic Fault Tree Analysis
CHMM - Coupled Hidden Markov Model
DHMM - Discrete Hidden Markov Model
LMS - Least Mean Square
MSE - Mean Square Error
ROC - Receiver Operating Characteristic
V - Voltage
I - Current
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LIST OF APPENDICES
APPENDIX TITLE PAGE
A Steady state voltages and currents waveforms 68-69
B Currents and voltages waveforms during fault 70-78
C Fault data for ANN training 79-82
D Data arrangement of ANN training 83-86
E Fault data for ANN additional testing 87-90
F Data arrangement of ANN additional testing 91-93
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CHAPTER 1
INTRODUCTION
1.1 Background
Power transformer is one of the most important equipment in electrical power
system and it has a great effect on electrical equipment’s security, operating cost and
national economic advance. Once failures for the large-scale power transformer
occur, it can result in tremendous economic losses and serious society impact [1].
Therefore, it is very important to detect the fault in power transformer as early as
possible so that the reliability of power system will be improved.
Power system stability depends on the reliable operation of various individual
components within the network. Power transformer is one of the necessary and
significant units in the transmission and distribution levels of a power system.
However, it is subjected to many different types of faults which may cause
interruptions in power supply; consequently result in serious economic losses as well
as social impacts. Several fault diagnosis methods only could able to detect all the
faults. Figure 1.1 shows type of fault in power transformer. As a result, effective
fault diagnosis approaches are warrant to detect and analyze the power transformer
internal faults, and eliminate the associated impacts to the lowest possible level.
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Faults in a transformer can be categorized as internal and external faults. External
faults include asymmetric fault, line-to-ground fault, line-to-line fault, and so on
while the internal faults include winding defects and winding insulation failures
resulting in turn-to- turn fault or ground fault. The insulation degradation may
happen because of several reasons including magnetizing inrush current, lightning
strikes, prolonged overload, and failure of cooling mechanism [2].
Figure 1.1 Types of fault in power transformer [2]
There are two main parts to address the problem when fault occur in power
transformer happen. First is detection of the fault along with its classification and
second is detecting the location of the fault. For detecting and classifying faults,
different faults patterns are required.
Nowadays, there are many expert systems for power transformer fault
diagnosis such as Artificial Neural Network (ANN), Support Vector Machine
(SVM), Genetic Algorithm (GA), Fault Tree Analysis (FTA), Hidden Markov Model
(HMM), Fuzzy and so on. Among of all expert study ANN is highly potential due to
it learns from examples presented during the training process and also the neural
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network operates as a pattern classifier and its ability to identify fault types and fault
location accurately.
1.2 Problem Statements
Power transformers require a proper maintenance with an approach of
efficient condition monitoring techniques and diagnostic tools. Fault diagnosis is a
challenging problem because there are numerous fault situations that can possibly
happen to an electrical transformer.
Nowadays there are so many methods that can be used for power transformer
fault diagnosis, but many researchers used data from Dissolved Gas Analysis (DGA)
as input for detection. DGA is a common practice in the incipient fault diagnosis of
power transformers but it has several limitations which is it cannot precisely localize
a fault and if the transformer has been refilled with fresh oil, results are not indicative
of faults. Sometimes, in DGA due to lack attention during management of oil
sampling and storage, gas-in-oil concentrations can present significant deviations
that may lead to misinterpretation of DGA and next, wrong diagnosis of fault in
power transformer.
Therefore, this project will focus on electrical measurement such as voltage
(V) and current (I). The research for this project will be done and simulations will be
carried out in order to do the fault diagnosis for the power transformer.
The method for power transformer fault diagnosis that will be used in this
study is Artificial Neural Network (ANN). The Neural Network is suitable for use
because of it significant advantages in important factors such as cost, speed of
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operation, reliability, ease of maintenance and ease of initial development. By using
ANN, the decision-making criteria are done by learning it from examples presented
during the training process and it can operate as a pattern classifier with ability to
identify fault location accurately.
1.3 Objectives
The objectives of this project are:
i. To model and simulate the power system network by using MATLAB
R2013a Simulink.
ii. To identify the fault in power transformer by using electrical
parameter such as voltage (V) and current (I).
iii. To diagnose the fault in power transformer by using Artificial Neural
Network (ANN).
1.4 Scope of Study
In this project, limitation works are based on power transformer external
fault, which are line-to-line, line-to-ground, and line-to-line-to-ground faults. The
first part is to study and simulate the fault in power transformer by using computer
software that is MATLAB R2013a Simulink. Star-star 132/20 kV transformer is used
for this simulation.
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The second part is to develop a system that can locate the fault in power
transformer by using ANN method. Simulation of ANN is done by using MATLAB
R2013a software. Parameters to be considered are the simulation input of the
network, which are the voltage and current values at primary and secondary of the
transformer, and also its output of fault location. The efficiency of the neural
network is to be tested and verified.
1.5 Methodology
For the methodology process, firstly need to do literature review to find a
suitable method to do fault diagnosis in power transformer. ANN was chosen
because of it was easy to apply for power system operators and also ANN learns by
examples, which is it can be trained with known example of a problem to acquire
knowledge and experiences.
The first stage is modeling a power system model with 132/20 kV
transformer by using MATLAB R2013a Simulink. Second stage is simulating and
analyzing the fault in the power transformer by electrical parameters that are voltage
and current at both side of transformer. The third stage is training an ANN using the
simulation data in MATLAB R2013a, and the last stage is evaluating the trained
ANN.
Next, proceed with the thesis writing or prepare the report. The report should
be complete with the elements starting from introduction until conclusion, and then
submit the report at the end.
! 6!
1.6 Structure of Thesis
!!!This report consists of six chapters, which are from this chapter until
appendices. There are summary of each chapter of this report.
Chapter 1 described about an introduction. In this chapter, an overview of this
project had been discussed which includes background, problem statements,
objectives, scopes of work and methodology.
Chapter 2 explained about the literature review. It presented an overview of
power transformer fault diagnosis methods.
Chapter 3 discussed on literature review on Artificial Neural Network method
that had been used to perform this project.
Chapter 4 explained about the methodology in this project. In this chapter the
possible faults had been simulated, and then the inputs and targets data arrangement
for ANN programming had been shown.
Chapter 5 explained about results and discussion. In this chapter, the result of
ANN Programming had been analyzed and discussed.
Chapter 6 described the conclusion of the project. The recommendation for
future works also suggested in this chapter.
!
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Junior, Ricardo A. S. Fernandes, Erasmo S. Neto, Danilo H. Spatti and José
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! 67!
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