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Corona fault detection in switchgear with extreme learning machine

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dc.citedby4
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.authorid57194057526en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid25824552100en_US
dc.date.accessioned2023-05-29T08:10:29Z
dc.date.available2023-05-29T08:10:29Z
dc.date.issued2020
dc.description.abstractSwitchgear is a very important component in a power distribution line. Failure in switchgear can lead to catastrophic danger and losses. In this research, a fault detection system is proposed with the implementation of Extreme Learning Machine (ELM). This algorithm is capable to identify faults in switchgear by analyzing the sound wave generated. Experiments are carried out to investigate the performance of the developed algorithm in identifying Corona faults in switchgears. The performances are analyzed in time and frequency domains, respectively. In time domain analysis, the results show 90.63%, 87.5%, and 87.5% of success rates in differentiating the Corona and non-Corona cases in training, validation and testing phases respectively. In frequency domain analysis, the results show 89.84%, 83.33%, and 87.5% success rates in training, validation and testing phases respectively. It can thus be concluded that the developed algorithm performed well in identifying Corona faults in switchgears. � 2020, Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/eei.v9i2.2058
dc.identifier.epage564
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85083066655
dc.identifier.spage558
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083066655&doi=10.11591%2feei.v9i2.2058&partnerID=40&md5=f7c963f863a34276061383ce94fbd1f9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25526
dc.identifier.volume9
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleBulletin of Electrical Engineering and Informatics
dc.titleCorona fault detection in switchgear with extreme learning machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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