Publication:
Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques

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dc.citedby5
dc.contributor.authorMohammed Alsumaidaee Y.A.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorChen C.P.en_US
dc.contributor.authorTan C.H.en_US
dc.contributor.authorAli K.en_US
dc.contributor.authorBalasubramaniam Y.A.L.en_US
dc.contributor.authorid58648412900en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57883616100en_US
dc.contributor.authorid56489158400en_US
dc.contributor.authorid36130958600en_US
dc.contributor.authorid57189520843en_US
dc.date.accessioned2024-10-14T03:18:39Z
dc.date.available2024-10-14T03:18:39Z
dc.date.issued2023
dc.description.abstractSwitchgear and control gear are susceptible to arc problems that arise from slowly developing defects such as partial discharge, arcing, and heating due to faulty connections. These issues can now be detected and monitored using modern technology. This study aims to explore the effectiveness of deep learning techniques, specifically 1D-CNN model, LSTM model, and 1D-CNN-LSTM model, in detecting arcing problems in switchgear. The hybrid model 1D-CNN-LSTM was the preferred model for fault detection in switchgear because of its superior performance in both time and frequency domains, allowing for analysis of the generated sound wave during an arcing event. To investigate the effectiveness of the algorithms, experiments were conducted to locate arcing faults in switchgear, and the time and frequency domain analyses of performance were conducted. The 1D-CNN-LSTM model proved to be the most effective model for differentiating between arcing and non-arcing situations in the training, validation, and testing stages. Time domain analysis (TDA) showed high success rates of 99%, 100%, and 98.4% for 1D-CNNen_US
dc.description.abstract99%, 100%, and 98.4% for LSTMen_US
dc.description.abstractand 100%, 100%, and 100% for 1D-CNN-LSTM in distinguishing between arcing and non-arcing cases in the respective training, validation, and testing phases. Furthermore, frequency domain analysis (FDA) also demonstrated high accuracy rates of 100%, 100%, and 95.8% for 1D-CNNen_US
dc.description.abstract100%, 100%, and 95.8% for LSTMen_US
dc.description.abstractand 100%, 100%, and 100% for 1D-CNN-LSTM in the respective training, validation, and testing phases. Therefore, it can be concluded that the developed algorithms, particularly the 1D-CNN-LSTM model in both time and frequency domains, effectively recognize arcing faults in switchgear, providing an efficient and effective method for monitoring and detecting faults in switchgear and control gear systems. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4617
dc.identifier.doi10.3390/app13074617
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85152712407
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85152712407&doi=10.3390%2fapp13074617&partnerID=40&md5=85a34bab289094510507259deb925d5b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34252
dc.identifier.volume13
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.subject1D-CNN-LSTM
dc.subjectarcing
dc.subjectdeep learning
dc.subjectenergy
dc.subjectfaults
dc.subjectswitchgear
dc.titleDetecting Arcing Faults in Switchgear by Using Deep Learning Techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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