Fault classification in a transmission line using discrete wavelet transform and artificial neural networks

Occurrence of fault in transmission line can cause loss of power to consumer. When fault occur, it is important to have a system that can identify the types of fault thus can make fast corrective action. The Matlab/Simulink is used to simulate four different types of fault signals in transmission li...

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Main Author:	Makerly, Harnetta Hashleynna
Format:	Thesis
Language:	English
Published:	2019
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/84045/1/HarnettaHashleynnaMSKE2019.pdf
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id	my-utm-ep.84045
record_format	uketd_dc
spelling	my-utm-ep.840452019-11-05T04:36:05Z Fault classification in a transmission line using discrete wavelet transform and artificial neural networks 2019-01 Makerly, Harnetta Hashleynna TK Electrical engineering. Electronics Nuclear engineering Occurrence of fault in transmission line can cause loss of power to consumer. When fault occur, it is important to have a system that can identify the types of fault thus can make fast corrective action. The Matlab/Simulink is used to simulate four different types of fault signals in transmission line which are Single Line to Ground (SLG), Double line to Ground (DLG), Line to Line (LL) and Three Phase Fault. The mother wavelet daubechies4 (db4) is used while using DWT for decomposition of these signals to obtain coefficient. The coefficient data sets which are obtained from the DWT is used as inputs for training and testing the ANN architecture. The overall accuracy of 95% is achieved using the DWT and BPNN technique after 62 iterations. The high success rate means the system have low mean squared error and is reliable to use for fault classification. The results indicate that the proposed scheme can correctly classify almost every possible fault types. 2019-01 Thesis http://eprints.utm.my/id/eprint/84045/ http://eprints.utm.my/id/eprint/84045/1/HarnettaHashleynnaMSKE2019.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126592 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 Makerly, Harnetta Hashleynna Fault classification in a transmission line using discrete wavelet transform and artificial neural networks
description	Occurrence of fault in transmission line can cause loss of power to consumer. When fault occur, it is important to have a system that can identify the types of fault thus can make fast corrective action. The Matlab/Simulink is used to simulate four different types of fault signals in transmission line which are Single Line to Ground (SLG), Double line to Ground (DLG), Line to Line (LL) and Three Phase Fault. The mother wavelet daubechies4 (db4) is used while using DWT for decomposition of these signals to obtain coefficient. The coefficient data sets which are obtained from the DWT is used as inputs for training and testing the ANN architecture. The overall accuracy of 95% is achieved using the DWT and BPNN technique after 62 iterations. The high success rate means the system have low mean squared error and is reliable to use for fault classification. The results indicate that the proposed scheme can correctly classify almost every possible fault types.
format	Thesis
qualification_level	Master's degree
author	Makerly, Harnetta Hashleynna
author_facet	Makerly, Harnetta Hashleynna
author_sort	Makerly, Harnetta Hashleynna
title	Fault classification in a transmission line using discrete wavelet transform and artificial neural networks
title_short	Fault classification in a transmission line using discrete wavelet transform and artificial neural networks
title_full	Fault classification in a transmission line using discrete wavelet transform and artificial neural networks
title_fullStr	Fault classification in a transmission line using discrete wavelet transform and artificial neural networks
title_full_unstemmed	Fault classification in a transmission line using discrete wavelet transform and artificial neural networks
title_sort	fault classification in a transmission line using discrete wavelet transform and artificial neural networks
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	http://eprints.utm.my/id/eprint/84045/1/HarnettaHashleynnaMSKE2019.pdf
_version_	1747818431955075072

Fault classification in a transmission line using discrete wavelet transform and artificial neural networks

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