a signal processing technique for pd detection...

A SIGNAL PROCESSING TECHNIQUE FOR PD DETECTION AND

LOCALIZATION IN POWER TRANSFORMER

MOHAMMED ABD ALI AZIZ

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 2014

iii

I would like to dedicate this thesis to my parents, my wife and my son

for their endless love and guidance which always helps me

to choose the right path

iv

ACKNOWLEDGMENT

First of all I would like to thank my dearest Allah (S.W.T) to make me able to

complete this research on time and answered my prayers at every steps of this

research. I want to express my deepest appreciation to my colleagues, my friends and

especially my family members who supported and prayed for me a lot, because of

whom my graduation has been complete successfully and smoothly.

I am very grateful to my supervisor Ass. Prof. Dr. Zulkurnain Abdul-Maiek,

who had positive attitude towards me and my work throughout this span of research

and guided me. His abundant help and knowledge allowed me to understand present

era issues related to my project and encourage me to work with dedication. This task

was not meaningful without his assistance and supervision because of his sage

advices and friendly attribute for me every time when I needed him the most.

I would also like to thank my dearest friends, my classmates and my

housemates to whom I passed most of the time during my graduate and faced every

ups and down of my studies. They were always there for me to give moral support

and always encourage me in let-down conditions.

Finally last but not the least, I would like to thank my father .He was always

there cheering me up and stood by me to encourage and gave every kind of support.

v

ABSTRACT

The activity of partial discharge (PD) is major problem that causes

degradation to the insulation system. Due to continuous stresses of high voltage it

may lead to complete breakdown of insulation system. Due to the high cost of

transformer windings, especially in the case of high kVA capacity, it is necessary to

find a way to localize the PD along the winding length so that only faulty part is

replaced instead of the whole winding. In this project a new technique of a digital

signal processing method based on the Fast Fourier Transformation (FFT) is applied

to locate the PD. The transformer winding was modelled in Matlab using an

equivalent lumped transformer circuit for each winding section. The PD source is

injected at different section, each section represent a different PD location. The

output current was then analysed using FFT. For validation, comparisons had been

made with the results of an experimental work on the same transformer winding

configuration as well as with theoretical equations. The work has shown that the

frequency spectra of the simulated output current have the same characteristic as that

for the measured signal as well as for the theoretically derived transfer function. It is

also shown that the crests and troughs in the frequency spectra could be used for

locating the source of the discharge activity in the power transformer winding. The

frequency of the zero in the simulated spectra increases as the discharge moves away

from the measuring terminal. The poles are however not affected by the position of

the PD source. An Artificial Neural Network (ANN) was successfully used to

determine the section number where PD occurs based on just the frequency response

of the output current. Further studies on the effects of the modelling parameters such

as variations in the capacitance and inductance values due to factors such as ageing

on the PD localization show that the capacitance is the most sensitive parameter in

the model. In addition, it is also shown that the effect of noise on the PD localization

can be eliminated using the Free Induction Decay (FID) technique.

vi

ABSTRAK

Aktiviti nyahcas separa (PD) adalah satu masalah besar yang menyebabkan

keburukan kepada sistem penebat. Kerosakan penuh pada sistem penebat boleh

berlaku disebabkan oleh tekanan berterusan voltan tinggi. Disebabkan harga lilitan

pengubah yang tinggi, terutama apabila melibatkan kapasiti kVA yang besar, satu

kaedah untuk menentukan lokasi PD sepanjang lilitan adalah sangat penting agar

hanya bahagian yang rosak sahaja perlu digantikan berbanding keseluruhan lilitan.

Projek ini memperkenalkan satu kaedah baru yang menggunakan pemprosesan

isyarat digital berasaskan ‗Fast Fourier Transformation‘ (FFT) bagi menentukan

lokasi PD. Lilitan pengubah dimodelkan dalam Matlab menggunakan litar setara

tergumpal bagi setiap seksyen lilitan. Sumber PD dikenakan pada setiap seksyen,

setiap seksyen mewakili satu lokasi PD. Arus keluaran kemudiannya dianalisis

menggunakan FFT. Untuk pengesahan, perbandingan telah dibuat dengan hasil

pengukuran dari pengubah yang mempunyai konfigurasi sama dan juga persamaan

teori. Didapati spektra frekuensi arus keluaran yang disimulasi mempuyai ciri yang

sama dengan ciri isyarat dari eksperimen dan dari rangkap pindah yang diterbitkan

secara teori. Puncak dan paluh spektra frekuensi boleh digunakan untuk menentukan

lokasi nyahcas separa di dalam lilitan pengubah kuasa. Frekuensi sifar pada spektra

frekuensi bertambah berkadar dengan lokasi PD menjauhi terminal pengukuran.

Walaubagaimanapun, frekuensi kutub tidak dipengaruhi oleh sumber PD. ‗Artifical

Neural Network‘ (ANN) telah digunakan dengan jayanya untuk menentukan nombor

seksyen lilitan di mana berlakunya PD berasaskan hanya sambutan frekuensi arus

keluaran. Kajian lanjutan ke atas kesan parameter model seperti perubahan kapasitan

dan keraruhan disebabkan oleh faktor seperti penuaan ke atas lokasi PD

menunjukkan kapasitan merupakan parameter model yang paling sensitif. Telah

ditunjukkan juga bahawa kesan hingar ke atas penentuan lokasi PD boleh dihapuskan

dengan menggunakan kaedah ‗Free Induction Decay‘ (FID).

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

1 INTRODUCTION 1

1.1 Introduction 1

1.2 Background 2

1.3 Problem Statement 4

1.4 Objective of Study 6

1.5 Scope of the study 6

2 LITERATURE REVIEW 7

2.1 Introduction 7

2.2 Detection and Localization PD Methods 8

2.2.1 Hybrid Transformer Model Method 8

2.2.2 Correlation Method 10

2.2.3 Acoustic Method 11

2.2.4 Electrical Method 13

2.2.5 Wave Travelling Method 19

2.2.6 The Propose Technique

viii

(Frequency Spectrum Method) 21

3 RESEARCH METHODOLOGY 22

3.0 Introduction 22

3.1 Flow Chart 23

3.1.1 Flow Chart of Methodology 23

3.1.2 Flow Chart of Propose Method 24

3.2 Transformer Winding Memory Theory 24

3.2.1 Matlab Software Implementation 25

3.3 Relationship between Bushing and Neutral 26

3.4 Digital Signal Processing 28

3.4.1 Fourier Transform 28

3.4.2 DFT 29

3.4.3 FFT 29

3.4.3.1 Fault Detection by FFT 29

3.5 Frequency Spectrum Analyses 30

3.5.1 Transfer Functions Characteristics of the

Transformer Parameters 32

3.5.2 Frequency Spectrum Features 34

3.5.3 PD detection and Localization by Poles

And Zeros Observation 35

3.6 Sensitivity analysis of Transformer Winding

Parameters Deviation 35

3.6.1 Effect of Capacitance 36

3.6.2 Effect of Inductance 36

3.6.3 Effect of Resistance 36

3.7 Denoising 36

3.8 Neural Network 38

3.8.1 Structure of a Feedforward Neural Network 38

3.8.2 Supervised Learning 42

3.8.3 Backpropagation and resilient back propagation 43

3.8.4 Curve Fitting 45

3.8.5 Presentation Data to Neural Network 48

ix

4 RESULTS AND DISCUSSION 54

4.1 Healthy Transformer 54

4.2 Partial Discharge detection and Localization Results 55

4.3 Validation 59

4.4 Transformer Winding Parameters Deviation 64

4.4.1 Effect of Capacitance 64

4.4.2 Effect of Inductance 65

4.4.3 Effect of Resistance 66

4.5 Denoising 70

4.6 Neural Network Signal Recognition 72

5 CONCLUSION AND FUTURE WORKS 76

5.1 Conclusion 76

5.2 Future work Recommendation 77

REFERENCES 78

x

LIST OF TABLES

TABLE NO. TITLE PAGE

4.1 The simulated result of fig (4.2), poles position (P)

of the transfer function frequency spectrum in kHz 58

4.2 The simulated result of fig (4.2), zeros position (Z)


4.3 The measured result of fig (4.3), poles position (P)


4.4 The simulated result of fig (4.4), poles position (P)


4.5 The measured result of fig (4.3), zeros position (Z)


4.6 The simulated result of fig (4.4), zeros position (Z)


4.7 The result of classification of the five PD source for

each ANN hidden layer 73

xi

LIST OF FIGURES

FIGURE NO TITLE PAGE

1.1 PD on high voltage transformer 5

2.1 Basic crossover coil 9

2.2 Multi conductor transmission line model 9

2.3 Relationship between two responses si(t) and rx(t) 11

2.4 Principle of the acoustic localization 12

2.5 Absolute and relative time in two-sensor-setup 12

2.6 Simplified transformer winding model 13

2.7 frequency characteristics of transformer windings marked

by the three main region. 16

2.8 Voltage ratio distributions of the impulse for the two winding 18

2.9 PD traveling through transformer winding 19

2.10 PD signal at the bushing and neutral terminal 20

3.1 Flow Chart of the Methodology 23

3.2 Flow Chart of the Proposed Method 24

3.3 Equivalent circuit of, inductor/capacitor ladder network 25

3.4 PD and transformer circuit parameters modelling using matlab

simulink 26

3.5 Linear relationship of the terminal response from frequency

response measurement 27

3.6 Ideal sampler 31

3.7 Measurement impedance of transformer winding circuit 33

3.8 Log-scale of the peridogram of secondary voltage signal for a

different PD signal at section1 34

3.9 Free Induction Decay (FID) technique which cans suppress

xii

the noise in spectrum wave form in frequency domain 37

3.10 Three- layer feed forward neral network 38

3.11 Three- layer feed forward neral network 39

3.12 Basic idea of the back propagation algorithm illustrated for a

unilabiate error function E(w) 43

3.13 A sample of one hundred observations of X and Y 47

3.14 The "true" curve for Y as well as the random X and Y values 47

3.15 Bias in sigmoid transfer function 49

3.16 Effect of temperature on sigmoid transfer function 50

3.17 A typical logistic curve has an inflection point at an X value

of 0 and f (x) value of .5 51

3.18 The regression fitted value and the neural network fitted value 52

4.1 The frequency spectrum of the healthy transformer 55

4.2 Shows the frequency spectrum of the transfer functions of 8

section PD source locations 57

4.3 Measured laboratory experiments and simulated result of the

frequency spectrum taken 60

4.4 The frequency spectrum results of the simulated work of the

proposed method for five origins in each section 61

4.5 Frequency spectrum deviation when the winding capacitance

reduced by 10% and 20% 65

4.6 Frequency spectrum deviation when the winding inductive

reduced by 10% and 20%. 66

4.7 Frequency spectrum deviation when the winding resistance

reduced to zero. 67

4.8 The distribution for the first three poles of the frequency

spectrum deviation for three capacitor cases (original,

reduced by 10% and reduced by 20%) 67

4.9 The distribution for the first three zeros of the frequency

spectrum deviation for three capacitor cases (original,

reducedby 10% and reduced by 20%) 68

4.10 The distribution for the first three poles of the frequency

spectrum deviation for three inductance cases (original,


xiii

4.11 The distribution for the first three zeros of the frequency

spectrum deviation for three inductance cases (original,


4.12 The distribution of the poles of the frequency spectrum

deviation for the two resistance cases (original, reduced to zero) 69

4.13 The distribution of the poles of the frequency spectrum

deviation for the two resistance cases (original, reduced to zero) 70

4.14 The three case waveshapes of the frequency spectrum for

section 1 (original signal. noisy signal and after filtering signal) 71

4.15 The two case waveshapes of the frequency spectrum for

section 2 (original signal. noisy signal and after filtering signal) 72

4.16 A 24-hidden Layer feedforward network with sigmoid hidden

neurons and linear output neurons 73

4.17 Neural network training progress window. 74

4.18 The evaluation of trained network . Best Validation is at epoch 2. 75

4.18 Regrression of the output with respect to the target fo training. 76

xiv

LIST OF SYMBOLS AND ABBREVIATIONS

Vref - Reference signal of PD

si(t) - Reference signals of the responses of winding si(t)

rx(t) - Test signals of the responses of winding rx(t)

tref - Reference pulse width in µs

ttest - Test PD pulse in µs

Pix - Correlation coefficient

α - Winding distribution factor

H(w) - Gain of the two end voltages in dB

vx - Voltage variation from the neutral to the distance x

K - Capacitance of inter-turn and inter-disc transformer

winding

C - Capacitance to ground including the capacitance of

the core and tan

L - Inductive branch of each turn conductor

R - Resistance branch of each turn conductor

CB - Bushing capacitance

l - Total length of winding

Cc - Capacitance of cylindrical void

Cb - capacitance of the remaining series insulation with

void

Ca - capacitance of the remaining discharge-free

insulation of the rest of the solid insulator

N - The number of samples

fs - Sampling frequency

N - The number of samples

fs - Sampling frequency

UL - Voltage at line end

IL - Current at line end

xv

UN - Voltage at neutral end

IN - Current at neutral end

CL - The line end capacitance

CN - The neutral end capacitance

a,b - Designed separable FIR filter coefficient vectors

ηk - learning rate ηk

∂E/∂w - Partial derivatives of the error function with respect

to the weights

W0 - The weight on the output connection

W1, W2. . . Wn - The weight on the input connection

X1, X2. . . Xn - n outside stimuli

y1, y2. . . yn - Output from the n neurons

IFFT - Invers Fourier Transform

TFL - Transfer Functions between the PD source and the

Line- End

TFN - The transfer Functions between the PD source and

the Neutral-End

FIR - Finite Impulse Response Digital Filter

IIR - Infinite Impulse Response Digital Filter

WGN - White Gaussian Noise

A/D - Analogue to Digital Convertor

Zoh - Zero Hold error

FID - Free Induction Decay

ANN - Artificial Neural Network

f() - a function

E - Error function

SSE - Sum of Squared Errors

MLP - Multi-Layer Perceptrons

BP - Back Propagation Algorithim

CHAPTER 1

INTRODUCTION

1.1 Introduction

There are many assets in electric power system, which are very expensive,

and the most significant and an expensive component in the grid station is the power

transformer. It is considered to be one of the essential and costly equipment that

operate like heart of the power system. Its insulation is very important and detection

of insulation faults is the best possible preventive measurements to avoid irreparable

damages. The life of Power transformer could be extended by insulation monitoring

through PD measurements. It is not only economically valuable, but also saves lots

of revenues. The PD activity is one of the most common phenomenon‘s that become

cause of insulation faults. Other sources are mechanical deformations, which leads to

defects in power transformers that can cause insulation failure [1]. Considering the

fact that early detection of PD can prevent complete failure of the transformer

insulation and breakdown, a precise modelling of partial discharge associated with

transformer winding in high frequencies mode has been performed. To reduce the

maintenance time and cost the exact detection of PD activity in the insulating

structure near the transformer windings has a great importance [2].

2

1.2 Background

Partial discharge (PD) is defined as localized electrical discharge within only

a part of the insulation between two separated conductors. In the real applications,

PD is caused by the existence of voids in the insulation. Even if the local electrical

field in the void exceeds a threshold and a dicharge occurs, it is limited within the

void due to the strong surrounding insulation, enough to avoid a complete

breakdown. PD in voids is considered harmful, especially in high-voltage systems

from the engineering viewpoint as they cause energy loss and gradually degrade the

insulation [1].

PD may occur in solid, liquid and gaseous insulation media and are generally

initiated by an excessive localized electric field. The PD induced current in an

external circuit depends on the nature of the discharge and the geometry of the

system [2]. In many cases, where there are electrically weak areas in solid dielectric

materials (most often gaseous or liquid inclusions), PD can be measured when

enough voltage is applied. PD phenomenon causes degradation in insulated

materials[3]. The investigation of insulated material behaviour under electrical stress

and the data obtained from PD measurements, provide the possibility to predict

dielectric breakdown. There are three main types of PD: (i) micro discharges in small

voids, which always exist both at the surface of electrodes and in the volume of

insulators; (ii) breakdowns along the boundaries between two insulators (typically

along the solid insulator–gas interface); and (iii) PD in the channels of branched

structures (streamers) propagating in the volume of a dielectric medium. The second

and third PD types can be considered as incomplete breakdown, since the insulating

properties of a dielectric are not violated[4,5].

Consequently, on-line PD measurement is a powerful condition monitoring

technique that is now being devcloped for use with power transformers. Techniques

for the location of a PD are of major importance in on-site maintenance and repair.

The PD pulse suffers distortion and attenuation as it travels from the site-of-origin to

3

the measuring terminal. Consequently, the measured signal is a highly distorted

representation of the original PD pulse, but it does contain useful information about

the location and nature of the discharge. If this information can he extracted and

analysed, the PD source may he located[6,7]. The different frequency ranges that

describe the spectra of the measured PD signal, the modes of propagation in the

transformer winding have different characteristics. In the lower frequency range (e.g.

0 to 0.0IMHz) the component of a PD pulse that propagates in the, winding is

predominantly an electromagnetic wave that travels along the galvanic path. In the

intermediate frequency range (e.g. 0.01 to 0.1MHz) the propagation is dominated by

the characteristic resonances of the winding. In the higher frequency range (e.g. 0.1

to 10 MHz) the propagation is mainly through the capacitive elements of the

winding[8,9].

The PD signals detected at the terminals of a transformer will have different

waveshapes due to the different propagation paths from the discharge source to the

terminals, even if the same type of discharge generates them.

In this project Matlab/Simulink simulation software would be used for the

detection and localization of PD phenomenon. The simulation studies have suggested

that the PD signals at the measurement terminals their propagation paths and the

transfer functions from the source of the discharge to the measuring terminal have

unique characteristics. The measurements conducted using PD calibrators on several

plain-disc-type windings A frequency spectrum representation was used during those

measurements. Studies carried out on the transfer function based PD location

techniques indicated the importance of developing these techniques into practical

applications.

4

1.3 Problem Statement

Some of transformer winding reaches to many thousand dollars specially in

the case of high KVA capacity. The popular test had been done by detecting the fault

like partial discharge without localizing the PD in the winding, and the problem

solved by replacing the whole winding and hence expensive cost. To overcome this

economical issue it is necessary to find a way to localize PD along the winding

length so that only faulty part is replaced instead of the whole winding. This concept

is very useful especially in a disk type transformer winding, when replacing a portion

of the winding instead of the whole winding.

Voluminous digital PD data are nowadays readily available with constant

improvement in PD measurement techniques. Power engineer may be able to detect

prominent PDs using oscilloscope and existing couplers. But identification of the

types of developing and random occurring PD is a real challenge to any practicing

engineer. In this thesis a new technique of a digital signal processing method such as

fast fourier transformation (FFT) is applied to analyse PD detection and localization

in transformer winding.

PD activities may cause deviation of the transformer winding parameters. The

deviation of the transformer parameters are due to many reasons like for example

heating during operation aging and deterioration of the winding insulation. Hence

reduces the sensitivity of the measurements and the uncertainties in the estimation of

transformer parameters.

When the winding was energised there was significant audible and electrical

noise due to core vibration. The result was an increase in the noise level in the time

domain signals. For the corresponding frequency spectra, the noise only affects the

spectra in the lower frequency range, i.e. below 50kHz. Various time and frequency

domain de-noising techniques are adopted for the extraction of PD signal. Signal

processing methods to post process the measurements have been utilised, resulting to

5

a rejection of the noise and improvement of the sensitivity.

Figure 1.1 PD on high voltage transformer

6

1.4 Objective of the study

i- Modelling the power transformer circuit winding parameters with partial

discharge (PD) injection.

ii- Demonstrate the concept of the detection and localization using FFT

transfer function. And classify the location using ANN.

iii- Studying the dynamic behaviour of winding parameters deviation.

iv- De noising the audible noise in transformer core using FID filtering

technique.

1.5 Scope of the study

i- Simulate a model for the transformer winding by a lumped circuit model

and PD pulses using Simulink/Matlab software.

ii- Analyse the PD location by FFT to the measured current responses from

the bushing and neutral terminals. After injecting a PD pulse into

different sections along the winding, then classify by ANN.

iii- Change the value of R,L and C to study the behaviour of winding

parameters deviation.

iv- De noising the noise in transformer core using FID filtering technique

After modelling the noise.

REFERENCES

[1] Tatizawa H. and Burani G. F., "Analysis and of Location Partial Discharge in

Power Transformer by Mean of Electrical Methods, ―Electrical distribution,

2001International Conference on, 2001, vol. 1, pp. 1-5.

[2] Gurumurthy G.R. and Kini S. S., “Simulation of PD location using power

transformer model winding,“IEEE Region 10 Colloquium and the Third

ICIIS, Kharagpur, INDIA December 2008, pp. 2-4.

[3] Kenroy A.Q., ―Developing a Stochastic Model for Partial Discharge

Detection In Voids of Polymeric Cable Insulation‖ University Technology

Malaysia: Master Thesis, 2006.

[4] Boggs, S.A. ―Partial discharge: overview and signal generation‖, IEEE

Electr. Insul. Mag., 1990, 6, (4), pp. 33–39.

[5] Kupershtokh A. L., Stamatelatos C. P., and Agoris D. P., “Simulation of

Partial Discharge Activity in Solid Dielectrics under AC Voltage” Vol. 32,

No. 8, pp. 680–683. © Pleiades Publishing, Inc., 2006.

[6] Gockenbach. E., “Detection and Localization of Partial Discharges in Power

Transformers”, Conf. Publications No.94, Part 1, IEE Diagnostic Testing of

HV Power Apparatus in Service, March 6-8, 1973, London.

[7] Fuhr I., Haessig M., and Boss P., ec al, “Detection and Location of Internal

Defects in the Insulation of Power Transformers”, IEEE transactions on

Electrical Insulation, vol. 28, No. 6, December 1993, pp. 1057-1067.

[8] T. Bertula, V. Palva, and E. Talvio, “Partial Discharge Measurement on

Oil-paper Insulated Transformers”, Report 12-7, CIGRE, 1968.

[9] James R.E., Phung, B. T., and Su Q., “Application of digital Filtering

Techniques to the Determination of Partial Discharge Location in

Transformers”, vol. 24, No.4, August 1989, pp.657-668.

80

[10] Guardado J. L., Venegas V., Melgoza E., and Naredo J. L., ―Modeling Pulse

Propagation in High Voltage Machines", 10th

Int. Sympos. High Voltage

Eng., Montreal, Canada, 1997, pp. 125-130.

[11] Naderi M. S., Vakilian M., Blackburn T.R., Phung B.T., Zhang H. and Naderi

M. S., "Simulation of partial discharge propagation and location in abetti

winding based on structural data," Power Engineering Conference, 2005, pp.

1-210.

[12] Mohammad S.N., Vakilian M., T.R. Blackburn, B.T. Phung, H. Zhang and

M. S. Naderi, “ A method for studying partial discharges location and

propagation within power transformer winding based on the structural data,“

International Symposium on Electrical Insulating Materials, Kitakyushu,

Japan, June 5-9,2005, pp. 491-492.

[13] Ji T.Y., Tang W.H. and Wu Q.H., “Partial disharge location using a hybrid

transformer winding model with morphology-based noise removal, “Electric

power system reseaarch 101(2013)9-16, science direct, journal home page:

www.elsevier.com/local/epsr, 2013, pp. 9-10.

[14] Jeyabalan V., ―Energy Based Correlation Method for Location of Partial

Discharge in Transformer Winding," Advances in Electrical and Computer

Engineering, 2009, vol. 9(1), pp. 46-51.

[15] Lathi B.P., “Modern digital and analog communication systems”, Oxford

university press, 2005.

[16] Jeyabalan V., and Usa S., “Frequency domain correlation techniques for

location of partial discharge in transformer windings‖, IEEE-IPEC -2007.

[17] Koochaki A., Kouhsari S. M., and Ghanavati G., ―Transformer Internal

Faults Simulation”, Advances in Electrical and Computer Engineering,

Suceava, Romania, No 2/2008, volume 8 (15), pp. 23-28.

[18] Palani A. and Jayashankar V., “Virtual instrument for lightning impulse

test,” IEEE Trans. Power Del., vol. 22, no. 3, pp. 1309–1317, Jul. 2007.

[19] Hettiwatte S. N., Crossley P. A., Wang Z. D., Darwin A. and Edwards G., "

Simulation of a transformer winding for partial discharge propagation

studies," Power Engineering Society, IEEE, 2002,vol. 2, pp. 1394-1399.

[20] Wang Z. D., Crossley P. A., and Cornick K. J., „An algorithm for partial

discharge location in distribution transformers‟, IEEE/PES Winter Meeting,

January 2000, Singapore.

http://www.elsevier.com/local/epsr

81

[21] Wang Z., Garcia V. C., and Cornick K., „Frequency spectra of partial

discharge pulses‟, Proc. 8th Asian Conf. on Electrical Discharge (ACED-96),

paper 209, Oct. 1996, Bangkok, Thailand.

[22] Lundgaard L.E., Hansen W., and Dursum K., "Location of partial discharge

location in power transformers using external acoustic sensore", 6th

International Symposium on High Voltage Engineering, New Orleans, USA,

1989.

[23] Olivieri M., “Use of acoustic signal measuring technique for detection and

location of Partial Discharges in Power Transformers” (in Portuguese),

MSc. Thesis, COPPE-UFRJ, Jud1999, Rio de Janeiro.

[24] James R.E., Phung B.T. and Su Q., "Applicationof digital filtering techniques

to the determination of partial discharge location in transformers", IEEE

Trans. Elect. Insul. Vol.24, No.4, pp. 657-668, 1989.

[25] Gockenbach E. and Borsi H., “Transfer function as tool for noise suppression

and localization of partial discharges in transformers during on-site

measurements," International Conference, 2008, pp. 1111-1114.

[26] Su Q., James R.E., Blackburn T., Phung T., Tychsen R. and Simpson J.,

"Development of a Computer-Based Measurement System for the Location of

Partial Discharges in Transformer and Hydro-Generator Windings,''

Australian Universities Power and Control Engineering Conference, 3-4

October, 1991, Melbourne.

[27] Wang Z. D., Crossley P. A., and Comick U.I., ―A Simulation Model for

Propagation of Partial Discharge Pulses in Power Transformers‖,

Proceedings of the International Conference on Power System Technology

(PowerCon-98), August, 18-21, 1998, Beijing, China, pp.151-155.

[28] Denis D., Wolfgang K., Stefan T., Ruben G. and Thomas K., “Wide and

narrow band PD detection in plug-in cable connectors in the UHF range”,

ICCMD, Beijing, China, April 21-24, 2008.

[29] Hettiwatte S.N., Wang Z.D. and Crossley P.A., ―Investigation of propagation

of partial discharges in power transformers and techniques for locating the

discharge," Science Measurement and Technology,IEEE ,2005, vol. 152 , pp.

25-30.

[30] Osgood B. , ―The fourier transform and its applications,“ Book, Electrical

Engineering Department Stanford University,June 8, 1999, pp. 20-21.

82

[31] Salih S.M, “fourier transform signal processing,―Book, InTech, april 11,

2012, pp. 164-166..

[32] Orfanidis S. J., ―Introduction to signal processing,― Book, Pearson Education,

2010, pp. 4-5.

[33] Mohammed R., and Lewin P. L., "Partial Discharge Location in High Voltage

Transformers," Electrical Insulation Conference, 2009, pp. 200-2004.

[34] James R. E., Phung B. T., and Su Q., ―Application of digital filtering

Techniques to the Determination of Partial Discharge Location in

Transformers," Electrical Insulation, IEEE Transactions, 1989, vol. 24, pp.

657-668.

[35] Wang Z.D.: “Location of partial discharges in power transformers”. PhD

Thesis, UMIST, Manchester, UK, 1999

[36] Wang Z.D., Crossley P.A., Cornick K.J., and Zhu, D.H.: „Partial discharge

location in power transformers‟, IEE Proc. Sci., Measure Technol., 2000,

147, (5), pp. 249–255

[37] Hettiwatte S.N., Wang Z.D., Crossley, P.A., Darwin, A., and Edwards, G.: ‗

Experimental investigation into the propagation of partial discharge pulses

in transformers‟, Presented at the IEEE Power Engineering Society Winter

Meeting, New York, USA, January 2002

[38] Debruyne, H., and Lesaint, O.: ‗A method to locate a PD source in a winding

by processing signals at its terminals‟. Proc. 12th Int. Symp. On High

Voltage Engineering (ISH‘2001), Bangalore, India, August 2001

[39] Debruyne, H., and Lesaint, O.: ‗A method to locate a PD source in a winding

by processing signals at its terminals‟. Proc. 12th Int. Symp. On High

Voltage Engineering (ISH‘2001), Bangalore, India, August 2001

[40] Eldery M. A., Abdel-Galil T. K., El-Saadany E. F. and Salama M. M. A.,

―Identification of partial discharge locations in transformer winding using

PSD estimation," Power Delivery, IEEE Transactions, 2006. vol. 21, pp.

1022-1023.

[41] Tajali R., and Square P.E., ―Line to ground voltage monitoring on

ungrounded and impedance grounded power systems, ―Power Systems

Engineering Group 295 Tech Park Drive LaVergne, Tennessee 37086, pp. 4-6

[42] Jeyabalan V., “Interpreting the frequency responses of PD signals for PD

location in transformer windings Gram schmidt orthonormalized method―,

83

IEEE 10th International Conference on the Properties and Applications of

Dielectric Materials, July 24-28, 2012, Bangalore, India

[43] Karris S. T., “Introduction to simulink with engineering applications, ―Book,

Orchard Publications, Library of Congress Control Number (LCCN)

2006926850, 2006.

[44] Kalechman M., ―Practical matlab applications for engineers,“ book,

Electrical and Telecommunication Engineering Technology New York City

College of Technology City University of New York (CUNY), 2009.

[45] Farrar, T. C., and Becker, E. D. (1971). Pulse and Fourier transform NMR:

introduction to theory and methods. Elsevier.

[46] Xu, S., Mitchell S. D., and Middleton, R. H. (2003). Partial Discharge

Localization for a Transformer Based on Frequency Spectrum

Analysis. Universidade de Queensland, Australia www. itee. uq. edu. Au.

[47] Francis L. (2001). Neural networks demystified. In Casualty Actuarial Society

Forum (pp. 253-320).

[48] Gunther F., and Fritsch, S. (2010). neuralnet: Training of neural

networks. The R Journal, 2(1), 30-38.

[49] Panchal, G., Ganatra, A., Shah, P., and Panchal, D. (2011). Determination of

over-learning and over-fitting problem in back propagation neural network.

International Journal on Soft Computing, 2(2), 40-51.

[50] Lawrence, S., Giles, C. L., and Tsoi, A. C. (1997, July). Lessons in neural

network training: Overfitting may be harder than expected.

In AAAI/IAAI (pp. 540-545).

[51] Bishop, C. M., and Roach, C. M. (1992). Fast curve fitting using neural

networks.Review of scientific instruments, 63(10), 4450-4456.

a signal processing technique for pd detection...

Documents