Publication:
Detecting surface discharge faults in switchgear by using hybrid model

Simple item page
dc.citedby1
dc.contributor.authorAlsumaidaee Y.A.M.en_US
dc.contributor.authorKoh S.P.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.authorid58648412900en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57883616100en_US
dc.date.accessioned2024-10-14T03:17:39Z
dc.date.available2024-10-14T03:17:39Z
dc.date.issued2023
dc.description.abstractSwitchgear plays a crucial role in power systems, providing protection and control over electrical equipment. However, tracking (surface discharge) can lead to insulation degradation and switchgear failure, necessitating reliable and effective identification of tracking defects. In this paper, we propose a hybrid one-dimension convolutional neural network long short-term memory networks (1D-CNN-LSTM) model as a solution to this problem. Data from both time domain analysis (TDA) and frequency domain analysis (FDA) are utilized for model evaluation. The model achieved error-free accuracy of 100% in both TDA and FDA during the training, validation, and testing phases. The model�s performance is further assessed using performance measures and the visualization of accuracy and loss curves. The results show that the hybrid 1D-CNN-LSTM model works well to accurately find and classify surface discharge tracking defects in switchgear. The model offers precise and dependable fault identification, which has the potential to significantly enhance switchgear functionality. By enabling proactive maintenance and timely intervention, the proposed model contributes to the overall reliability and performance of switchgear in power systems. The findings of this research provide valuable insights for the design and implementation of advanced fault detection systems in switchgear applications. � 2023 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v32.i1.pp413-422
dc.identifier.epage422
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85174191481
dc.identifier.spage413
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174191481&doi=10.11591%2fijeecs.v32.i1.pp413-422&partnerID=40&md5=2ca14addd8c331a475ce140420228368
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34011
dc.identifier.volume32
dc.pagecount9
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.subject1D-CNN-LSTM
dc.subjectEnergy
dc.subjectSurface charge
dc.subjectSwitchgear faults
dc.subjectTracking
dc.titleDetecting surface discharge faults in switchgear by using hybrid modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS