Publication:
Fault Identification in Power Transformers Using Dissolve Gas Analysis and Support Vector Machine

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dc.citedby3
dc.contributor.authorIllias H.A.en_US
dc.contributor.authorKai Choon C.en_US
dc.contributor.authorLiang W.Z.en_US
dc.contributor.authorMokhlis H.en_US
dc.contributor.authorAriffin A.M.en_US
dc.contributor.authorFairouz Mohd Yousof M.en_US
dc.contributor.authorid26633053900en_US
dc.contributor.authorid57226568375en_US
dc.contributor.authorid57200387475en_US
dc.contributor.authorid8136874200en_US
dc.contributor.authorid16400722400en_US
dc.contributor.authorid36547024400en_US
dc.date.accessioned2023-05-29T09:06:48Z
dc.date.available2023-05-29T09:06:48Z
dc.date.issued2021
dc.descriptionNeural networks; Power transformers; Support vector machines; Artificial intelligence techniques; Cost of maintenance; Dissolved gas analyses (DGA); Fault identifications; Life span; Training and testing; Transformer faults; Dielectric materialsen_US
dc.description.abstractTransformer faults need to be identified accurately at the early stage in order to ease the maintenance of power transformer, reduce the cost of maintenance, avoid severe damage on transformer and extend the lifespan of transformer. Dissolved Gas Analysis (DGA) is the most commonly used method to identify the transformer fault in power system. However, the existing transformer fault identification methods based on DGA have a limitation because each method is only suitable for certain conditions. Thus, in this work, one of the artificial intelligence techniques, which is Support Vector Machine (SVM), was applied to determine the power transformer fault type based on DGA data. The accuracy of the SVM was tested with different ratio of training and testing data. Comparison of the results from SVM with artificial neural network (ANN) was done to validate the performance of the system. It was found that fault identification in power transformers based on DGA data using SVM yields higher accuracy than ANN. Therefore, SVM can be recommended for the application of power transformer fault type identification in practice. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9493970
dc.identifier.doi10.1109/ICPADM49635.2021.9493970
dc.identifier.epage36
dc.identifier.scopus2-s2.0-85111961755
dc.identifier.spage33
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111961755&doi=10.1109%2fICPADM49635.2021.9493970&partnerID=40&md5=99d08f1a9a8688adbf4aceb52c91b759
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26100
dc.identifier.volume2021-July
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials
dc.titleFault Identification in Power Transformers Using Dissolve Gas Analysis and Support Vector Machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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