Publication:
Arcing Faults Detection in Switchgear with Extreme Learning Machine

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dc.contributor.authorIshak S.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTan J.D.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorChen C.P.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorid57194057526en_US
dc.contributor.authorid57883863700en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57883616100en_US
dc.contributor.authorid36560884300en_US
dc.date.accessioned2023-05-29T09:39:53Z
dc.date.available2023-05-29T09:39:53Z
dc.date.issued2022
dc.descriptionFrequency 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 detectionen_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12007
dc.identifier.doi10.1088/1742-6596/2319/1/012007
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85137693001
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137693001&doi=10.1088%2f1742-6596%2f2319%2f1%2f012007&partnerID=40&md5=359fd873c61c063d62b9fbbe422132bf
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27124
dc.identifier.volume2319
dc.publisherInstitute of Physicsen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleJournal of Physics: Conference Series
dc.titleArcing Faults Detection in Switchgear with Extreme Learning Machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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