Publication:
Lightning Fault Classification for Transmission Line Using Support Vector Machine

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dc.citedby1
dc.contributor.authorAsman S.H.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorKadir M.Z.A.A.en_US
dc.contributor.authorAmirulddin U.A.U.en_US
dc.contributor.authorRoslan N.en_US
dc.contributor.authorElsanabary A.en_US
dc.contributor.authorid57194493395en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid25947297000en_US
dc.contributor.authorid26422804600en_US
dc.contributor.authorid57205233093en_US
dc.contributor.authorid57221120034en_US
dc.date.accessioned2024-10-14T03:21:21Z
dc.date.available2024-10-14T03:21:21Z
dc.date.issued2023
dc.description.abstractTransmission lines are susceptible to a variety of phenomena that can cause system faults. The most prevalent cause of faults in the power system is lightning strikes, while other causes may include insulator failure, tree or crane encroachment. In this study, two machine learning algorithms, Support Vector Machine (SVM) and k-Nearest Neighbor (kNN), were used and compared to classify faults due to lightning strikes, insulator failure, tree and crane encroachment. The input variables for the models were based on the root mean square (RMS) current duration, voltage dip, and energy wavelet measured at the sending end of a line. The proposed method was implemented in the MATLAB/SIMULINK programming platform. The classification performance of the developed algorithms was evaluated using confusion matrix. Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN's 70.60%. Moreover, SVM also outperformed k-NN in terms of computational time, with time taken by SVM is 3.63 s compared to 10.06 s by k-NN. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/APL57308.2023.10181525
dc.identifier.scopus2-s2.0-85166739771
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85166739771&doi=10.1109%2fAPL57308.2023.10181525&partnerID=40&md5=832772eb5c1227dd7187f9e2083f9348
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34642
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleAPL 2023 - 12th Asia-Pacific International Conference on Lightning
dc.subjectaccuracy
dc.subjectcomputational time
dc.subjectk-Nearest Neighbor (k-NN)
dc.subjectlightning fault
dc.subjectSupport Vector Machine (SVM)
dc.subjectElectric lines
dc.subjectElectric power transmission
dc.subjectLearning algorithms
dc.subjectMATLAB
dc.subjectMotion compensation
dc.subjectNearest neighbor search
dc.subjectTransmissions
dc.subjectAccuracy
dc.subjectComputational time
dc.subjectFailure trees
dc.subjectFault classification
dc.subjectK-near neighbor
dc.subjectLightning faults
dc.subjectLightning strikes
dc.subjectSupport vector machine
dc.subjectSupport vectors machine
dc.subjectTransmission-line
dc.subjectSupport vector machines
dc.titleLightning Fault Classification for Transmission Line Using Support Vector Machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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