Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar

Transmission line with fast and accurate fault classification is very important for protection and safety manner. The presence of distorted signals will cause the transmission line fail to classify definitely the fault that occurred. There are many impacts if the failure to classify fault in transmi...

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Main Author:	Abd Ghafar, Muhamad Azfar
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Wiring Transmission lines
Online Access:	http://ir.uitm.edu.my/id/eprint/14495/ http://ir.uitm.edu.my/id/eprint/14495/1/TD_MUHAMAD%20AZFAR%20ABD%20GHAFAR%20EE%2015_5.pdf

id	uitm-14495
recordtype	eprints
spelling	uitm-144952018-03-01T02:45:18Z http://ir.uitm.edu.my/id/eprint/14495/ Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar Abd Ghafar, Muhamad Azfar Wiring Transmission lines Transmission line with fast and accurate fault classification is very important for protection and safety manner. The presence of distorted signals will cause the transmission line fail to classify definitely the fault that occurred. There are many impacts if the failure to classify fault in transmission line happen such as waste of time, high maintenance cost, and the device itself will having problem. Thus, it is important to provide a good fault classification in transmission line even though the signals are distorted by noise. This paper present a study of fault classification in transmission line with a combination of wavelet transform (WT) and single layer feed-forward network (SLFN) trained by Extreme Learning Machine (ELM) algorithm. The WT is used to decompose the input of three-phase current signal produced by the three-phase transmission line model and extract it into desired features. In this paper, the energy and mean features are been selected. The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. Classification performance of SLFN is evaluated by using two types of dataset which are dataset without noise and another dataset with Signal to ratio (SNR) 30 dB. The results of this work show that fault classification using SLFN trained by ELM has high accuracy on estimating fault in transmission line system. 2015 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/14495/1/TD_MUHAMAD%20AZFAR%20ABD%20GHAFAR%20EE%2015_5.pdf Abd Ghafar, Muhamad Azfar (2015) Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Wiring Transmission lines
spellingShingle	Wiring Transmission lines Abd Ghafar, Muhamad Azfar Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
description	Transmission line with fast and accurate fault classification is very important for protection and safety manner. The presence of distorted signals will cause the transmission line fail to classify definitely the fault that occurred. There are many impacts if the failure to classify fault in transmission line happen such as waste of time, high maintenance cost, and the device itself will having problem. Thus, it is important to provide a good fault classification in transmission line even though the signals are distorted by noise. This paper present a study of fault classification in transmission line with a combination of wavelet transform (WT) and single layer feed-forward network (SLFN) trained by Extreme Learning Machine (ELM) algorithm. The WT is used to decompose the input of three-phase current signal produced by the three-phase transmission line model and extract it into desired features. In this paper, the energy and mean features are been selected. The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. Classification performance of SLFN is evaluated by using two types of dataset which are dataset without noise and another dataset with Signal to ratio (SNR) 30 dB. The results of this work show that fault classification using SLFN trained by ELM has high accuracy on estimating fault in transmission line system.
format	Thesis
author	Abd Ghafar, Muhamad Azfar
author_facet	Abd Ghafar, Muhamad Azfar
author_sort	Abd Ghafar, Muhamad Azfar
title	Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
title_short	Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
title_full	Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
title_fullStr	Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
title_full_unstemmed	Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
title_sort	fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / muhamad azfar abd ghafar
publishDate	2015
url	http://ir.uitm.edu.my/id/eprint/14495/ http://ir.uitm.edu.my/id/eprint/14495/1/TD_MUHAMAD%20AZFAR%20ABD%20GHAFAR%20EE%2015_5.pdf
first_indexed	2023-09-18T22:51:45Z
last_indexed	2023-09-18T22:51:45Z
_version_	1777417590636806144

Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar

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