Detection of arcing fault in underground distribution cable using artificial neural network

Arcing faults can cause substantial damage if they are not detected and isolated promptly. Detection of arcing faults has always been a difficult issue. Those faults tend to be of high fault resistance and hence the fault current is well below maximum load limit and its detection is not possible thr...

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Main Author:	Chan, Wei Kian
Format:	Thesis
Language:	English
Published:	2004
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/7994/1/ChanWeiKianMFKE2004.pdf
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id	my-utm-ep.7994
record_format	uketd_dc
spelling	my-utm-ep.79942018-09-19T05:07:10Z Detection of arcing fault in underground distribution cable using artificial neural network 2004-05 Chan, Wei Kian TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science Arcing faults can cause substantial damage if they are not detected and isolated promptly. Detection of arcing faults has always been a difficult issue. Those faults tend to be of high fault resistance and hence the fault current is well below maximum load limit and its detection is not possible through the use of overcurrent relays. In the case of overhead lines, the gas generated through arcing is dispersed rapidly. But in the case of underground cables, the generated gas could travel along cable duct and could result in explosion at manhole location, which is dangerous to personnel. The damage can be reduced if arcing faults are detected before they develop into major faults. The general aim of this study is to develop an arcing fault detection algorithm which can detect the presence of arcing fault in underground distribution cable. Arcing faults data are collected through simulations and experiments. The simulations involve the modelling of a simple underground distribution system and two TNB underground distribution systems using Power System Computer Aided Design 1 Electromagnetic Transient for Direct Current (PSCADIEMTDC) program. On the other hand, the experiments are conducted in research laboratory. The data collected from the simple underground distribution system are analysed in both time domain and frequency domain to identify the characteristics of arcing fault. A Multi-layer Perceptron (MLP) with Backpropagation (BP) learning is used to discriminate arcing faults from normal load condition. The detection results revealed satisfactory performance in all test cases. 2004-05 Thesis http://eprints.utm.my/id/eprint/7994/ http://eprints.utm.my/id/eprint/7994/1/ChanWeiKianMFKE2004.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:11515 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering TK Electrical engineering Electronics Nuclear engineering Chan, Wei Kian Detection of arcing fault in underground distribution cable using artificial neural network
description	Arcing faults can cause substantial damage if they are not detected and isolated promptly. Detection of arcing faults has always been a difficult issue. Those faults tend to be of high fault resistance and hence the fault current is well below maximum load limit and its detection is not possible through the use of overcurrent relays. In the case of overhead lines, the gas generated through arcing is dispersed rapidly. But in the case of underground cables, the generated gas could travel along cable duct and could result in explosion at manhole location, which is dangerous to personnel. The damage can be reduced if arcing faults are detected before they develop into major faults. The general aim of this study is to develop an arcing fault detection algorithm which can detect the presence of arcing fault in underground distribution cable. Arcing faults data are collected through simulations and experiments. The simulations involve the modelling of a simple underground distribution system and two TNB underground distribution systems using Power System Computer Aided Design 1 Electromagnetic Transient for Direct Current (PSCADIEMTDC) program. On the other hand, the experiments are conducted in research laboratory. The data collected from the simple underground distribution system are analysed in both time domain and frequency domain to identify the characteristics of arcing fault. A Multi-layer Perceptron (MLP) with Backpropagation (BP) learning is used to discriminate arcing faults from normal load condition. The detection results revealed satisfactory performance in all test cases.
format	Thesis
qualification_level	Master's degree
author	Chan, Wei Kian
author_facet	Chan, Wei Kian
author_sort	Chan, Wei Kian
title	Detection of arcing fault in underground distribution cable using artificial neural network
title_short	Detection of arcing fault in underground distribution cable using artificial neural network
title_full	Detection of arcing fault in underground distribution cable using artificial neural network
title_fullStr	Detection of arcing fault in underground distribution cable using artificial neural network
title_full_unstemmed	Detection of arcing fault in underground distribution cable using artificial neural network
title_sort	detection of arcing fault in underground distribution cable using artificial neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2004
url	http://eprints.utm.my/id/eprint/7994/1/ChanWeiKianMFKE2004.pdf
_version_	1747814717468966912

Detection of arcing fault in underground distribution cable using artificial neural network

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