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Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

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dc.citedby17
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.authorYusaf T.en_US
dc.contributor.authorAbdalla A.N.en_US
dc.contributor.authorAli K.en_US
dc.contributor.authorRaj A.A.en_US
dc.contributor.authorid58648412900en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57883616100en_US
dc.contributor.authorid23112065900en_US
dc.contributor.authorid25646071000en_US
dc.contributor.authorid36130958600en_US
dc.contributor.authorid57189492851en_US
dc.date.accessioned2024-10-14T03:19:07Z
dc.date.available2024-10-14T03:19:07Z
dc.date.issued2023
dc.description.abstractThe damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3108
dc.identifier.doi10.3390/s23063108
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85151199257
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151199257&doi=10.3390%2fs23063108&partnerID=40&md5=4f5c3789b867e384739a6cc88591a6d4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34334
dc.identifier.volume23
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleSensors
dc.subject1D-CNN-LSTM
dc.subjectcorona discharge
dc.subjectenergy
dc.subjectfaults
dc.subjectswitchgear
dc.subjectAir quality
dc.subjectElectric corona
dc.subjectFault detection
dc.subjectFlashover
dc.subjectLearning systems
dc.subjectLong short-term memory
dc.subjectTime domain analysis
dc.subject1d-CNN-LSTM
dc.subjectCorona discharges
dc.subjectDamaging effects
dc.subjectElectrical equipment
dc.subjectElectrical stress
dc.subjectEnergy
dc.subjectFault
dc.subjectMedium voltage
dc.subjectMetal-clad
dc.subjectTime and frequency domains
dc.subjectalgorithm
dc.subjectarticle
dc.subjectdeep learning
dc.subjecthuman
dc.subjectlearning
dc.subjectlong short term memory network
dc.subjectphysiological stress
dc.subjectsound
dc.subjectworker
dc.subjectFrequency domain analysis
dc.titleDetection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methodsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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