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Optimization of cable fault recognition system using particle swarm optimization

In this paper, a Partial Discharge (PD) based cable fault recognition system has been constructed using Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference System (ANFIS). The cable fault recognition system can perform well under noise free condition but...

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Main Authors: Raymond W.J.K., Jing C.H., Kuan T.M.
Other Authors: 55193255600
Format: Article
Published: World Academy of Research in Science and Engineering 2023
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spelling my.uniten.dspace-256612023-05-29T16:12:23Z Optimization of cable fault recognition system using particle swarm optimization Raymond W.J.K. Jing C.H. Kuan T.M. 55193255600 57219417197 49561583600 In this paper, a Partial Discharge (PD) based cable fault recognition system has been constructed using Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference System (ANFIS). The cable fault recognition system can perform well under noise free condition but endures performance deterioration when PD noise contamination is present. Particle Swarm Optimization (PSO) was used to enhance the performance of classifiers under noise contamination. A performance review has been done to compare the optimized and unoptimized cable fault recognition under noise contamination. Results show that PSO optimized cable fault recognition systems perform better compared to unoptimized cable fault recognition systems. Among the optimized cable fault recognition systems, ANN outperforms SVM and ANFIS. � 2020, World Academy of Research in Science and Engineering. All rights reserved. Final 2023-05-29T08:12:22Z 2023-05-29T08:12:22Z 2020 Article 10.30534/ijeter/2020/2381.12020 2-s2.0-85092674088 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092674088&doi=10.30534%2fijeter%2f2020%2f2381.12020&partnerID=40&md5=8b9881be26b87d4c29566537ed325cf6 https://irepository.uniten.edu.my/handle/123456789/25661 8 1 Special Issue 1 23 147 152 All Open Access, Bronze World Academy of Research in Science and Engineering Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description In this paper, a Partial Discharge (PD) based cable fault recognition system has been constructed using Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference System (ANFIS). The cable fault recognition system can perform well under noise free condition but endures performance deterioration when PD noise contamination is present. Particle Swarm Optimization (PSO) was used to enhance the performance of classifiers under noise contamination. A performance review has been done to compare the optimized and unoptimized cable fault recognition under noise contamination. Results show that PSO optimized cable fault recognition systems perform better compared to unoptimized cable fault recognition systems. Among the optimized cable fault recognition systems, ANN outperforms SVM and ANFIS. � 2020, World Academy of Research in Science and Engineering. All rights reserved.
author2 55193255600
author_facet 55193255600
Raymond W.J.K.
Jing C.H.
Kuan T.M.
format Article
author Raymond W.J.K.
Jing C.H.
Kuan T.M.
spellingShingle Raymond W.J.K.
Jing C.H.
Kuan T.M.
Optimization of cable fault recognition system using particle swarm optimization
author_sort Raymond W.J.K.
title Optimization of cable fault recognition system using particle swarm optimization
title_short Optimization of cable fault recognition system using particle swarm optimization
title_full Optimization of cable fault recognition system using particle swarm optimization
title_fullStr Optimization of cable fault recognition system using particle swarm optimization
title_full_unstemmed Optimization of cable fault recognition system using particle swarm optimization
title_sort optimization of cable fault recognition system using particle swarm optimization
publisher World Academy of Research in Science and Engineering
publishDate 2023
_version_ 1806426373277876224
score 13.211869

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