Detection of insulation faults in transformer using wavelet analysis

Recognition of key insulation faults in power transformers through impulse testing was certainly not seen as a big problem as it had emerged today but talking of minor faults which are often neglected after the impulse testing by the naked eye had been a challenging task for a very long time in powe...

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Main Author:	Asghar, Ali
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/38179/1/AliAsgharMFKE2013.pdf
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id	my-utm-ep.38179
record_format	uketd_dc
spelling	my-utm-ep.381792018-04-12T05:41:53Z Detection of insulation faults in transformer using wavelet analysis 2013-01 Asghar, Ali TK Electrical engineering. Electronics Nuclear engineering Recognition of key insulation faults in power transformers through impulse testing was certainly not seen as a big problem as it had emerged today but talking of minor faults which are often neglected after the impulse testing by the naked eye had been a challenging task for a very long time in power transformers. Hence there is seen a need of such a tool which should be capable of verifying the signals/waves after these di-electric tests, as the recognition of such faults is immensely essential to overcome any disastrous situation in the longer run. This work proposes an influential approach which is proficient in detecting such minor faults. The methodology uses wavelet analysis technique, the dyadic-orthonormal wavelet transform (DOWT) in particular. The principle idea behind the working is to detect the fault (noise) at the particular time instance after decomposition of recorded faulty current responses into detailed and smoothed description of the usual signal. The results showed that for three different frequency scales i.e. 10-5MHz for scale 1, 5-2.5MHz for scale 2 and 2.5-1.25MHz for scale 3, higher localized filter coefficient i.e. L=45 is seen to be much more efficient in detecting the fault at a particular instant than the L= 8 filter coefficients under the dyadic-orthogonal wavelet transform function. Therefore the projected technique proved to be robust and way far efficient as compared to the other methods to resolve such group of faults. 2013-01 Thesis http://eprints.utm.my/id/eprint/38179/ http://eprints.utm.my/id/eprint/38179/1/AliAsgharMFKE2013.pdf application/pdf en public 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
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Asghar, Ali Detection of insulation faults in transformer using wavelet analysis
description	Recognition of key insulation faults in power transformers through impulse testing was certainly not seen as a big problem as it had emerged today but talking of minor faults which are often neglected after the impulse testing by the naked eye had been a challenging task for a very long time in power transformers. Hence there is seen a need of such a tool which should be capable of verifying the signals/waves after these di-electric tests, as the recognition of such faults is immensely essential to overcome any disastrous situation in the longer run. This work proposes an influential approach which is proficient in detecting such minor faults. The methodology uses wavelet analysis technique, the dyadic-orthonormal wavelet transform (DOWT) in particular. The principle idea behind the working is to detect the fault (noise) at the particular time instance after decomposition of recorded faulty current responses into detailed and smoothed description of the usual signal. The results showed that for three different frequency scales i.e. 10-5MHz for scale 1, 5-2.5MHz for scale 2 and 2.5-1.25MHz for scale 3, higher localized filter coefficient i.e. L=45 is seen to be much more efficient in detecting the fault at a particular instant than the L= 8 filter coefficients under the dyadic-orthogonal wavelet transform function. Therefore the projected technique proved to be robust and way far efficient as compared to the other methods to resolve such group of faults.
format	Thesis
qualification_level	Master's degree
author	Asghar, Ali
author_facet	Asghar, Ali
author_sort	Asghar, Ali
title	Detection of insulation faults in transformer using wavelet analysis
title_short	Detection of insulation faults in transformer using wavelet analysis
title_full	Detection of insulation faults in transformer using wavelet analysis
title_fullStr	Detection of insulation faults in transformer using wavelet analysis
title_full_unstemmed	Detection of insulation faults in transformer using wavelet analysis
title_sort	detection of insulation faults in transformer using wavelet analysis
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/38179/1/AliAsgharMFKE2013.pdf
_version_	1747816523810996224

Detection of insulation faults in transformer using wavelet analysis

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