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Arcing Faults Detection in Switchgear with Extreme Learning Machine

Frequency domain analysis; Knowledge acquisition; Time domain analysis; Arcing; Arcing faults; Extreme learning machine; Fault sensing; Faults detection; General efficiencies; Learning machines; Power-distribution system; Switchgear fault; Validation stages; Fault detection

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Bibliographic Details
Main Authors: Ishak S., Koh S.P., Tan J.D., Tiong S.K., Chen C.P., Yaw C.T.
Other Authors: 57194057526
Format: Conference Paper
Published: Institute of Physics 2023
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id my.uniten.dspace-27124
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spelling my.uniten.dspace-271242023-05-29T17:39:53Z Arcing Faults Detection in Switchgear with Extreme Learning Machine Ishak S. Koh S.P. Tan J.D. Tiong S.K. Chen C.P. Yaw C.T. 57194057526 57883863700 38863172300 15128307800 57883616100 36560884300 Frequency domain analysis; Knowledge acquisition; Time domain analysis; Arcing; Arcing faults; Extreme learning machine; Fault sensing; Faults detection; General efficiencies; Learning machines; Power-distribution system; Switchgear fault; Validation stages; Fault detection The robustness of switchgears has critical impacts on the general efficiency of power distribution systems. Faulty switchgears lead to many unwanted complications for utility bodies, which in turn lead to even bigger issues. In this paper, a remote arcing fault sensing technique is proposed using ELM. By analysing the sonic waves emitted, the proposed method is capable to detect possible arcing faults in switchgears. Tests and experiments have been conducted to investigate the performance of the proposed algorithm in detecting these arcing faults. The obtained results are analysed in time and frequency domains. In the time domain analysis, the results show 93.75% success rate in training stage, 95.83% in validation stage, and 87.5% in testing stage. In the frequency domain analysis, the results show 93.75% success rate in training stage, 91.67% in validation stage, and 100% success rate in testing stage. It is thus concluded that the proposed algorithm is capable to identify arcing faults in switchgears. � Published under licence by IOP Publishing Ltd. Final 2023-05-29T09:39:53Z 2023-05-29T09:39:53Z 2022 Conference Paper 10.1088/1742-6596/2319/1/012007 2-s2.0-85137693001 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137693001&doi=10.1088%2f1742-6596%2f2319%2f1%2f012007&partnerID=40&md5=359fd873c61c063d62b9fbbe422132bf https://irepository.uniten.edu.my/handle/123456789/27124 2319 1 12007 All Open Access, Gold Institute of Physics 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 Frequency domain analysis; Knowledge acquisition; Time domain analysis; Arcing; Arcing faults; Extreme learning machine; Fault sensing; Faults detection; General efficiencies; Learning machines; Power-distribution system; Switchgear fault; Validation stages; Fault detection
author2 57194057526
author_facet 57194057526
Ishak S.
Koh S.P.
Tan J.D.
Tiong S.K.
Chen C.P.
Yaw C.T.
format Conference Paper
author Ishak S.
Koh S.P.
Tan J.D.
Tiong S.K.
Chen C.P.
Yaw C.T.
spellingShingle Ishak S.
Koh S.P.
Tan J.D.
Tiong S.K.
Chen C.P.
Yaw C.T.
Arcing Faults Detection in Switchgear with Extreme Learning Machine
author_sort Ishak S.
title Arcing Faults Detection in Switchgear with Extreme Learning Machine
title_short Arcing Faults Detection in Switchgear with Extreme Learning Machine
title_full Arcing Faults Detection in Switchgear with Extreme Learning Machine
title_fullStr Arcing Faults Detection in Switchgear with Extreme Learning Machine
title_full_unstemmed Arcing Faults Detection in Switchgear with Extreme Learning Machine
title_sort arcing faults detection in switchgear with extreme learning machine
publisher Institute of Physics
publishDate 2023
_version_ 1806426714657521664
score 13.214268

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