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Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning

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dc.citedby1
dc.contributor.authorAlsumaidaee Y.A.M.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.authorAli K.en_US
dc.contributor.authorid57903740900en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid57883863700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57883616100en_US
dc.contributor.authorid36130958600en_US
dc.date.accessioned2023-05-29T09:36:33Z
dc.date.available2023-05-29T09:36:33Z
dc.date.issued2022
dc.descriptionCondition based maintenance; Condition monitoring; Deep learning; Electric power transmission networks; Fault detection; Testing; Ultrasonic applications; 'current; Arcing; Condition-based monitoring; Deep learning; Energy distribution networks; Faults detection; Medium voltage; Medium voltage switchgears; Monitoring system; Power energy; Partial dischargesen_US
dc.description.abstractIn power energy distribution networks, switchgear is considered critical equipment. This is because the act of monitoring the operation and condition of switchgear, as well as performing the required corrective maintenance on any potentially problematic equipment, is critical. A single event may harm thousands of customers over time and pose a significant risk to operational staff. Many considerations must be put in place before using outages to switch down the system since they may raise maintenance costs and disrupt the power supply to users. As a result, proper interpretation of switchgear status evaluations is critical for the early identification of possible faults. Existing ultrasound condition-based monitoring (CBM) diagnostic testing techniques require the tester to manually interpret test data. This study aims to review the status of the recent development of CBM techniques with faults in switchgear. The current trend in electrification will be toward the safety and sustainability of power grid assets, which involves an evaluation of the electrical systems� and components� health and grids for medium-voltage distribution. This work provides a current state-of-the-art evaluation of deep learning (DL)-based smart diagnostics that were used to identify partial discharges and localize them. DL techniques are discussed and categorized, with special attention given to those sophisticated in the last five years. The key features of each method, such as fundamental approach and accuracy, are outlined and compared in depth. The benefits and drawbacks of various DL algorithms are also examined. The technological constraints that hinder sophisticated PD diagnostics from being implemented in companies are also recognized. Lastly, various remedies are suggested, as well as future research prospects. � 2022 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6762
dc.identifier.doi10.3390/en15186762
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85138713163
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85138713163&doi=10.3390%2fen15186762&partnerID=40&md5=6d5d0d63ee2d95325cf2e721f76d410c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26756
dc.identifier.volume15
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleReview of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learningen_US
dc.typeReviewen_US
dspace.entity.typePublication
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