Assessment of ESDD on High-Voltageinsulators Using Artificialneuralnetwork

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Electric Power Systems Research 72 (2004) 131136

Assessment of ESDD on high-voltage insulatorsusing artificial neural network

Ahmad S. Ahmad a,, P.S. Ghosh a, S. Shahnawaz Ahmed b, Syed Abdul Kader Aljunid a

a College of Engineering, Universiti Tenaga Nasional, Selangor, Malaysiab Department of Electrical and Electronics Engineering, Bangladesh University of En gineering and Technology, Dhaka 1000, Bangladesh

Received 12 November 2003; received in revised form 10 March 2004; accepted 13 March 2004

Available online 15 June 2004

Abstract

The environmental and weather conditions cause flashover on polluted insulators leading to outages in a power system. It is generally

recognized that the main causes leading to the contamination of insulators are marine pollution as found in the immediate neighborhood of

the coastal regions, and solid pollution as found in the dense industrial areas. This research is directed towards the study of contamination of

insulator under marine pollution. The effects of various meteorological factors on the pollution severity have been investigated thoroughly.

A new approach using ANN as a function estimator has been developed and used to model accurately the relationship between ESDD with

temperature (T), humidity (H), pressure (P), rainfall (R), and wind velocity (WV). The ANN-predicted ESDDs have been compared with the

measured ones for a practical system.

2004 Elsevier B.V. All rights reserved.

1. Introduction

With the ever increasing demand for electrical power,

there has been a steady growth in transmission line voltages

required for optimum and economic transfer of large blocks

of power over long distances. As the level of transmission

voltage is increased, switching and dynamic overvoltages

and withstand ability of the insulators under polluted con-

ditions have become most important factors in determining

the insulation level of the system. At the coastal areas the

high-voltage insulators are affected by salt particles that set-

tle on the insulators surfaces. The winds that blow from the

sea carry the salt particles. These particles are not danger-

ous in its dry condition but with high environmental humid-

ity or drizzle rain conditions the salt can absorb the wa-

ter and form a thin film with high conductivity. This layer

gives an ideal path for the leakage current to pass from the

high-voltage conductor to the ground. The conductivity of

this layer depends on the type of salts [1,2] that form the

layer on the insulators. High failure rate of polluted insu-

Corresponding author. Tel.: +60-3892-872-76;

fax: +60-3892-635-06.

E-mail address: [email protected] (A.S. Ahmad).

lator due to the flashover has been found near the coastalareas [3].

Contamination monitoring is important for addressing an

effective solution against pollution flashover. Meteorologi-

cal conditions vary considerably from the coastal areas to

the inland areas and play an important role in the deposi-

tion rate of pollutants and electrical behavior of insulators.

The survey [3] proved that the insulator contamination prob-

lem is strongly environment dependent and no generalized

anti-pollution criteria can be offered. The determination of

outdoor insulation level and design, in a new location is

a difficult matter without having some information on the

severity of prevailing pollution. It is, therefore, imperative to

have a reasonable and accurate assessment of site severity.

Works in Refs. [49] have found considerable relation-

ships between the contamination severity in terms of equiv-

alent salt deposit density (ESDD) and flashover with respect

to the meteorological parameters. But at the most the re-

search can study the effect of one or more of these param-

eters on ESDD or on flashover. An attempt has been done

in Ref. [10] to relate most of the meteorological parameters

with ESDD and develop a new mathematical model using

multiple regression analysis technique. However, regression

analysis cannot capture to the full extent the uncertainties in

an unknown relationship like that of ESDD.

0378-7796/$ see front matter 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.epsr.2004.03.009


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132 A.S. Ahmad et al. / Electric Power Systems Research 72 (2004) 131136

In the last decade many research have been carried out on

the application of ANN in many fields that require modeling

the uncertainties. It has been used successfully in capaci-

tor control [11], finding complex electric stress distribution

along the insulator surface [12], alarm processing [13], pat-

tern recognition of partial discharges [14,15] and in pollu-

tion discharge modeling [16]. In this paper a new approachusing ANN as function estimator has been developed and

used to model accurately the relationship between ESDD

(the dependent factor) and most of the meteorological pa-

rameters (the independent factors) such as temperature, hu-

midity, pressure, rainfall, and wind speed. The multi-layer

feed forward neural network is employed in this study. The

approach has been tested for a practical and live system sited

in the East Coast of Malaysia and compared with actual field

data.

2. Onsite measurements and research methodology

The pollution severity at a test location is quantified in

terms of equivalent salt deposit density stated in units of

mg/cm2 NaCl. ESDD is the equivalent amount of NaCl that

would yield the same conductivity at complete dilution. The

interpretation of ESDD data may be different from place to

place but its value provides a basis of classification of con-

tamination severity. ESDD is widely used in Malaysia for

determining salt contamination level. If the value of ESDD

is equal or greater than 0.03 mg/cm2, the insulators are then

washed [17]. The site measurement activities are carried

out daily during dry season at Sultan Ismail Power Station

in Paka, Terengganu, Malaysia, utilizing three samples oftypical tension type Cap-and-Pin glass insulators which are

commonly installed on transmission lines in that area. The

samples were taken down from the scaffold and the pollu-

tants were removed by washing the insulators using paint-

brush and distilled water. Every contaminated sample for

each test was washed by immersing it in distilled water and

the contamination value was measured by determining the

conductivity or the rate of rise of the conductivity value for

the polluted water after washing the insulator. Using such

procedure, ESDD can be determined. The knowledge about

contamination behavior and level will help in the establish-

ment of maintenance policy because both critical months

and exposure periods of the year can be obtained.

The pollution severity is measured in terms of ESDD

under five varying meteorological factors, e.g. temperature

(T), humidity (H), pressure (P), rainfall (R), and wind ve-

locity (WV). Efficient modeling of pollution severity and

flashover voltage is of paramount interest to all engineers in-

volved in the design of transmission line insulators. Among

the various artificial neural network presented so far, the

multi-layer feed-forward network with back-propagation

technique is employed in the present study to model

ESDD = f(T, H, R, P, WV). The neural network is trained

with the help of data obtained from site measurement and

the training accuracy has been assessed by root mean square

error (RMSE).

3. Details of ANN algorithm

Artificial neural network algorithm has been used suc-cessfully in many applications. It is useful because it acts as

a model of real-world system or function. The model then

stands for the system it represents, typically to predict or to

control it. ANN can model a function even if the equation

describing it is unknown; the only prerequisite is represen-

tative sample of the function behavior and that is from the

experimental data and not from a theoretical understanding.

Fig. 1 shows the schematic diagram of a multi-layer feed

forward network used in this paper. The neurons in the net-

work can be divided into three layers: input layer, output

layer, and hidden layers.

It is important to note that the feed forward network sig-

nals can only propagate from the input layer to the output

layer through the hidden layers. Each neuron of the output

layer receives a signal from all input via hidden layer neu-

rons along connections with modifiable weights. The neural

network can identify input pattern vectors, once the connec-

tion weights are adjusted by means of the learning process.

The back-propagation learning algorithm [18] which is a

generalization of WidrowHoff error correction rule [19] is

the most popular method in training the ANN and is em-

ployed in this work. This learning algorithm is presented

here in brief. For each neuron in the input layer, the neuron

outputs are given by

Oi = neti (1)

HIDDENLAYER

INPUT

LAYER

OUTPUTLAYER

INPUTS (R, WV, P, T, H)

DESIRED OUTPUTS ESDD

Fig. 1. The structure of a multi-layer neural network.


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A.S. Ahmad et al. / Electric Power Systems Research 72 (2004) 13 1136 133

where neti is the input of neuron i and Oi the output of

neuron i. Again, for each neuron in the output layer, the

neuron inputs are given by

netk =

Nj

j=1

wkjOj, k = 1, . . . , N k (2)

where wkj is connection weight between neuron j and neuron

k, and Nj, Nk are the number of neurons in the hidden and

output layers respectively; the neuron outputs are given by

Ok =1

1 + exp((netk + k)/0)= fk(netk, k, 0) (3)

where k is the threshold of neuron k, and the activation

functions fk a sigmoidal function. For the neurons in the

hidden layer, the input and the outputs are given by the

relationships similar to those given in the Eqs. (2) and (3)

respectively. The connection weights of the feed forward

network are derived from the inputoutput patterns in the

training set by the application of generalization delta rule

[18]. The algorithm is based on minimization of the error

function of each pattern p by the use of the steepest descent

method [18]. The sum of squared errors which is the error

function of each pattern is given by

Ep =1

2

Nk

k=1

(tpk Opk)2 (4)

where tpk and Opk are target and calculated outputs for out-

put neuron k respectively. The overall measure of the error

for all the inputoutput patterns is given by

E =

Np

p=1

Ep (5)

where Np is the number of inputoutput patterns in the train-

ing set. When an input pattern p with the target output vec-

tor tp is presented, the connection weights are updated by

using the equations

wkj(p) = pkOpj + wkj(p 1) (6)

where is learning rate and the momentum constant. Now,

pk is defined in two different ways. For each neuron in the

output layer

pk= (tpk Opk)Opk(1 Opk) (7)

and for each neuron in the hidden layer

pk= Opj(1 Opj)

Nk

k=1

pkwkj (8)

It is important to know that the threshold of each neuron is

learned in the way same as that for the other weights. The

threshold of a neuron is regarded as a modifiable connection

weight between that neuron and a fictitious neuron in the

previous layer, which always has an output value of unity.

4. Pre-processing of data

Scaling of the inputoutput data has a significant influ-

ence on the convergence property and also on the accuracy

of the learning process. It is obvious from the sigmoidal

activation function given in Eq. (3) that the range of the

output of the network must be within (0, 1). Moreover, theinput variables should be kept small in order to avoid sat-

uration effect caused by the sigmoidal function. Thus, the

inputoutput data must be normalized before the initiation

of training of the neural network. In this work nine different

schemes have been tried for scaling the inputoutput vari-

ables as detailed in the Ref. [16]. After the normalization,

the input variables can then be easily made to fall in the

range (1, 1). Further, the range of the normalized output

vector component is made to fall within (0, 1).

5. Problem description

The proposed modeling of pollution severity in terms of

ESDD is done with the help of data obtained from site mea-

surement performed at Sultan Ismail Power Station. The

meteorological parameters on which the value of ESDD de-

pends are: temperature in C, humidity in %, air pressure

in mbar, rainfall in mm2, and wind velocity in m/s. In this

paper, ESDD = f(T, H, P,R, WV) modeling has been at-

tempted based on artificial neural network instead of any

empirical approach. Out of 60 data sets collected from site

measurement, 46 sets of input/output patterns are used as

training data set in the training process. Each training pre-

sentation contains five input nodes characterizing meteoro-logical parameters (T, H, P, R, WV) and one output node

which provides corresponding values of ESDD. Once the

neural network is trained by using 46 training sets, it is

tested using four test data set selected randomly from the

remaining 14 data set. All inputs and outputs in the train-

ing patterns are normalized within the respective ranges as

per different normalizing schemes, before they are used for

neural network training and testing. Finally, with the help

of input pattern vectors of rest 10 data set estimated val-

ues of ESDD are computed using the trained ANN model,

i.e. ESDD = f(T, H, P, R, WV) and are plotted against the

measured ESDD values as shown in Fig. 2.

6. Details of work done

In applying the learning rule described, there are several

issues which should be addressed. The optimization process

has been carried out based on RMSE and less oscillation in

the error of convergence. From the results in Tables 15, the

observations that can be made are:

(a) For the convention learning algorithm with the choice

of = 0.2, = 0.9 and 11 hidden layers nodes, the


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Fig. 2. Comparison between estimated and measured values of ESDD.

Table 1

No. of hidden layers = 1, no. of hidden layer nodes = 11, = 0.2,

= 0.9, no. of iteration = 1000

Scheme no. Input Output RMSE

1 Max Max 0.0558

2 Max Maximin 0.0903

3 Max Mean and S.D. 0.0903

4 Maximin Max 0.0513

5 Maximin Maximin 0.0814

6 Maximin Mean and S.D. 0.0814

7 Mean and S.D. Max 0.0362

8 Mean and S.D. Maximin 0.0730

9 Mean and S.D. Mean and S.D. 0.0730

Table 2

No. of hidden layers = 1, no. of hidden layer nodes = 11, input = mean

and S.D., output = Max, no. of iteration = 1000

RMSE Notes

0.2 0.9 0.0362 High oscillation

0.2 0.8 0.0365 Less oscillation

0.2 0.7 0.0386

0.1 0.8 0.0398

0.25 0.8 0.0366

0.3 0.8 0.0366

Table 3

No. of hidden layers = 1, = 0.2, = 0.8, input = mean and S.D.,

output = Max, no. of iteration = 1000

No. of nodes in hidden layers RMSE Notes

9 0.0362 High oscillation

10 0.0375

11 0.0365 Less oscillation

12 0.0366

13 0.0363 High oscillation

Table 4

No. of hidden layer nodes = 11, = 0.2, = 0.8, input = mean and

S.D., output = Max, no. of iteration = 1000

No. of layers No. of nodes in hidden layers RMSE Notes

1 11 0.0365 Oscillation

2 11, 11 0.0383 No oscillation

best normalization scheme is being optimized in Table 1.

The number of iterations used in the training process is

1000. The results of Table 1 indicate that the scheme 7

of normalization is the best choice for the present work.

(b) Most of the works on feed forward neural nets use con-

stant values of and . Rumelhart et al. [18] recom-

mended that a combination of = 0.25, = 0.9 canyield good results for most problems. But there is still

no consensus as to what values of and should be

used in the learning process; rather, the optimal values

of and may be problem dependent. In the present

work, extensive studies have been carried out on the ef-

fect of different values of and on the convergence

rate of the learning method and are given in Table 2. It

is evident from Table 2 that the best RMSE is obtained,

i.e. 0.0362 for = 0.2 and = 0.9, but with high os-

cillation in the error convergence.

Whereas for = 0.2 and = 0.8 the RMSE is

slightly higher, i.e. 0.0365, but with less oscillation in

the error convergence. So the combination of = 0.2and = 0.8 is the best choice with 11 nodes in the hid-

den layer. The number of iterations used in the training

process is 1000. Fig. 3 shows the comparison between

the high oscillation and less oscillation in the error con-

vergence for the two cases shown in Table 2.

(c) As evident from Table 3 the number of hidden layer

nodes is not a constant factor, rather, it is also problem

dependent. The number of hidden layer nodes is varied

from 9 to 13. As seen from the result of Table 3, the best

RMSE is obtained with nine nodes but the convergence

is highly oscillatory. Whereas, the RMSE with 11 nodes

is slightly higher but the convergence of error is lessoscillatory. Thus, for the present work, on the basis of

minimum RMSE with less oscillation, the number of

nodes in the hidden layer is optimized at 11 with the

choice on = 0.2 and = 0.8.

(d) Table 4 compares the effect of number of hidden lay-

ers on the convergence rate of the training process. It is

found that, using two hidden layers has definitely a bet-

ter effect on the convergence rate than when one hidden

layer is used, with same number of hidden layer nodes in

both cases. When two hidden layers are used, although

the RMS error is increased slightly but the error conver-

gence is free of oscillation. Thus, in the present work,

the test output results are calculated, using two hidden

layers with 11 nodes in each.

(e) The discussion in Tables 14 reveals that excellent con-

vergence characteristic for the present work is obtained

with two hidden layers each containing 11 nodes and

= 0.2 and = 0.8. The modeled output of the test

data computed with help of best combination of the

modifiable parameters are tabulated against the target

output, that is, data obtained from site measurements in

Table 5. The RMS error obtained in the training pro-

cess for 5000 iterations is 0.0338 and the mean absolute

error (MAE) of the model output is found to be 3.6%.


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A.S. Ahmad et al. / Electric Power Systems Research 72 (2004) 13 1136 135

Table 5

No. of hidden layers = 2, no. of hidden layer nodes = 11, = 0.2, = 0.8, input = mean and S.D., output = Max, no. of iteration = 5000,

RMSE = 0.0338

No. of iteration

= 5000; RMSE

= 0.0338 rain (R)

(mm2); range: 053

Wind velocity,

WV (m/s);

range: 19

Pressure, P

(mbar); range:

10171027

Humidity, H (%);

range: 7492

Temperature, T

(C); range:

2530

ESDD measured

value (mg/cm2);

range:

0.0010770.047808

ESDD

estimate value

(mg/cm2)

MAE

(%)

0 2.5 1022 81 27.5 1.693E03 1.697E03 3.60 3 1024 80 28.25 1.739E03 1.682E03

0 4 1020 77 29.5 1.739E03 1.938E03

0.1 4.5 1021 83.5 28 2.267E03 2.259E03

Fig. 3. Comparison between high oscillation and less oscillation in error convergence for the two cases shown in Table 2.

Moreover, measured and estimated data of ESDD have

been plotted for ten tests chosen randomly from the data

collected at the site as shown in Fig. 2. It is also evident

from Fig. 2 that the proposed ANN model is superior

as compared to conventional regression model [10]. To

Fig. 4. The variation of RMS error vs. number of iterations during training

process.

examine the convergence characteristics of the conven-

tional algorithm, the variations of the root mean square

(RMS) errors in the learning process is depicted in Fig. 4

against number of iterations.

7. Conclusion

In this paper, ANN has been applied successfully in pol-

lution severity measurement studies for function estima-

tion. Modeling of the complex nonlinear function ESDD =

f(T, H, R, P, WV), the equation of which is unknown, has

been accomplished accurately. Further comparative analysis

of the estimated results with the measured data (not used in

training of ANN) collected from the site measurement amply

demonstrate the effectiveness of the use of ANN in model-

ing ESDD that has an unknown nonlinear relationship. The

estimation of critical contamination level in terms of ESDD

will help in fixing maintenance policy and addressing an ef-

fective solution against pollution flashover of high-voltage

insulators.


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References

[1] R.W.S. Garcia, R. Bosignli, E. Gomes Jr., Influence of the

non-uniformity of pollution distribution on the electrical behavior of

insulators, in: Proceedings of the Third International Conference on

Properties and Applications of Dielectric Materials, Tokyo, Japan,

812 July 1991, pp. 342345.

[2] K. Chrzan, Z. Pohl, T. Kowalak, Hygroscopic properties of pollutantson HV insulators, IEEE Trans. Electr. Insul. 24 (1) (1989) 107112.

[3] Q. Li, L. Wang, Z. Su, Y. Liu, K. Morita, R. Matsuoka, S. Ito, Natural

contamination test results of various insulators under DC voltage in

an inland area in China, in: Proceedings of the Third International

Conference on Properties and Applications of Dielectric Materials,

Japan, 812 July 1991, pp. 350353.

[4] V.T. Morgan, Effects of frequency, temperature, compression and air

pressure on the dielectric properties of a multilayer stack of dry kraft

paper, IEEE Trans. Dielectr. Electr. Insul. 5 (1) (1998) 125131.

[5] K. Naito, Y. Mizuno, W. Naganawa, A study on probabilistic as-

sessment of contamination flashover of high voltage insulator, IEEE

Trans. Power Deliv. 10 (3) (1995) 13781384.

[6] O.E. Gouda, Influence of pollution on HV Insulators, in: Conference

Record of the 1990 IEEE International Symposium on Electrical

Insulation, Toronto, Canada, 36 June 1990, pp. 195198.[7] J.C. Zheng, Z. Wang, Y.W. Liu, Influence of humidity on flashover

in air in the presence of dielectric surfaces, in: Proceedings of IEEE

Conference Region 10 on Computer Communication, Control and

Power Engineering, TENCON93, pp. 443449.

[8] Z. Renyu, Z. Jianchao, Progress in outdoor insulation research in

China, IEEE Trans. Electr. Insul. 25 (6) (1990) 11251137.

[9] X. Liu, J. Bai, Selection of insulation level of HVAC power lines of

operating in high altitude polluted area, in: Proceedings of the Second

IEEE International Conference on Properties and Applications of

Dielectric Materials, vol. 1, 1988, pp. 268271.

[10] A.S. Ahmad, H. Ahmad, R.B. Jidin, T. Tamsir, S. Shahnawaz Ahmed,

Z. Buntat, Contamination of high voltage insulators in the East Coast

of Peninsular Malaysia, in: Proceedings of the IEEEPES/CSEE In-

ternational Conference on Power System Technology, POWERCON

2000, vol. 3, Perth, Australia, 47 December 2000, pp. 12231228.[11] N.I. Santoso, O.T. Tan, Neural net based real time control of ca-

pacitors installed on the distribution system, IEEE Trans. PWRD 5

(1990) 266272.

[12] K. Bhattacharya, S. Chakravorti, P.K. Mukherjee, Insulator contour

optimization by a neural network, IEEE Trans. Dielectr. Electr. Insul.

8 (2) (2001) 157161.

[13] E.H.P. Chan, Application of neural network computing in intelligent

alarm processing, in: Proceedings of IEEEPICA Conference, Seatle,

USA, 1989, pp. 246251.

[14] H. Suzuki, T. Endoh, Pattern recognition of partial discharge in

XLPE cables using a neural network, IEEE Trans. Electr. Insul. 27

(1992) 543549.

[15] N. Hozumi, T. Okamoto, T. Imajo, Discrimination of partial discharge

patterns using a neural network, IEEE Trans. Electr. Insul. 27 (1992)

550556.[16] P.S. Ghosh, S. Chakravorti, Chatterjee, Estimation of time to flashover

characteristics of contaminated electrolytic surfaces using artifi-

cial neural network, IEEE Trans. Dielectr. Electr. Insul. 2 (1995)

10641074.

[17] H. Ahmad, M.Y. Bin Ibrahim, A multivariate model for equivalent

salt deposit density ESDD distribution prediction for the maintenance

of polluted insulator, in: Proceedings of the CIGRE Symposium,

Bangkok, 1989.

[18] D.E. Rumelhart, G.E. Hinton, J.R. Williams, Learning Internal Rep-

resentation by Error Propagation, Parallel Distributed Processing,

vol. 1, MIT Press, MA, 1986, pp. 318362.

[19] B. Widrow, M.E. Hoff, Adaptive Switching Networks, Parallel Dis-

tributed Processing, vol. 1, MIT Press, MA, 1986, pp. 123134.

Ahmad S. Ahmad was born in Diyala, Iraq.He received his B.Sc. in Electrical Engineering

from the University of Technology-Baghdad in

1987. He got his Master Degree from Universiti

Teknologi Malaysia (UTM) in 1999. He is IEEE

member. Currently he is a Senior Lecturer at the

Universiti Tenaga Nasional-Malaysia (UNITEN).

He is also in the final stage of his Ph.D.

P.S. Ghosh he received his B.Sc. in Electrical En-

gineering from R.E.C. Durgapur, Burdwan Uni-

versity in 1988. In 1990 he received his M.Sc. inElectrical Engineering on HV Engineering from

Jadavpur University, India. He received his Ph.D.

in Electrical Engineering on Pollution Flashover

Phenomenon of HV Transmission Line Insula-

tors from Jadavpur University, India in 1995.

Currently he is a Senior Lecture at Universiti

Tenaga Nasional-Malaysia.

S. Shahnawaz Ahmed received his B.Sc. and M.Sc. in electrical and

electronic engineering from Bangladesh University of Engineering and

Technology (BUET), Dhaka, respectively in 1982 and 1984, and his Ph.D.

in electrical engineering from the University of Manchester Institute of

Science and Technology, Manchester, U.K., in 1987. Since 1983, he

has been with BUET where he became a full Professor in 1996. He

also served on contract as a Professor in Universiti Teknologi Malaysia(UTM) in the period 20002003. Presently he also holds an additional

responsibility as the Director, Centre for Energy Studies, BUET. His

research interests include modeling, simulation, real-time control and

protection of power systems, FACTS, SMES, and photovoltaic arrays. He

has reviewed and authored many papers in international journals including

IEE Proceedings and IEEE Transactions. He is a Senior Member in IEEE

Power Engineering Society, and a Fellow in Bangladesh Institution of

Engineers.

Syed Abdul Kader Aljunid was born in Malaysia.

He received his Diploma in Electrical Engi-

neering (Light Current) from Technical College,

Malaysia in 1966. He received his B.Sc. (1stClass Hons.) in Electrical Engineering from the

University of Strathcylde, UK, 1969. He got his

M.Sc. in System Engineering from University

of Surrey, UK, 1973. He received his Ph.D. in

Electrical Engineering from University of Cali-

fornia Santa Barbara, USA, 1989. Currently he

is Professor in EE and also Special Advisor to the Vice Chancellor of

Universiti Tenaga Nasional.

Assessment of ESDD on High-Voltageinsulators Using Artificialneuralnetwork

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