Publication:
Fault Detection for Medium Voltage Switchgear Using a Deep Learning Hybrid 1D-CNN-LSTM Model

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dc.citedby4
dc.contributor.authorAlsumaidaee Y.A.M.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorChen C.P.en_US
dc.contributor.authorYusaf T.en_US
dc.contributor.authorBenedict F.en_US
dc.contributor.authorKadirgama K.en_US
dc.contributor.authorHong T.C.en_US
dc.contributor.authorAbdalla A.N.en_US
dc.contributor.authorid58648412900en_US
dc.contributor.authorid58168727000en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57883616100en_US
dc.contributor.authorid23112065900en_US
dc.contributor.authorid57194591957en_US
dc.contributor.authorid12761486500en_US
dc.contributor.authorid58486311800en_US
dc.contributor.authorid25646071000en_US
dc.date.accessioned2024-10-14T03:20:45Z
dc.date.available2024-10-14T03:20:45Z
dc.date.issued2023
dc.description.abstractMedium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2023.3294093
dc.identifier.epage97589
dc.identifier.scopus2-s2.0-85164727551
dc.identifier.spage97574
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164727551&doi=10.1109%2fACCESS.2023.3294093&partnerID=40&md5=cbdd10ffca1efa2e2667dfcf65e3cc7b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34571
dc.identifier.volume11
dc.pagecount15
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectarcing fault
dc.subjectdeep learning
dc.subjectEnergy
dc.subjectfault detection
dc.subjecthybrid model
dc.subjectmedium voltage switchgear
dc.subjectpower system safety
dc.subjectAccident prevention
dc.subjectDeep learning
dc.subjectElectric power system control
dc.subjectFault detection
dc.subjectFinite difference method
dc.subjectFrequency domain analysis
dc.subjectArcing faults
dc.subjectCorona
dc.subjectDeep learning
dc.subjectEnergy
dc.subjectFaults detection
dc.subjectFaults diagnosis
dc.subjectHybrid model
dc.subjectMedium voltage
dc.subjectMedium voltage switchgears
dc.subjectPower system safeties
dc.subjectUltrasonic imaging
dc.titleFault Detection for Medium Voltage Switchgear Using a Deep Learning Hybrid 1D-CNN-LSTM Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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