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Comparison of artificial neural network and multiple regression for partial discharge sources recognition

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dc.citedby1
dc.contributor.authorAbubakar Masud A.en_US
dc.contributor.authorMuhammad-Sukki F.en_US
dc.contributor.authorAlbarracin R.en_US
dc.contributor.authorAlfredo Ardila-Rev J.en_US
dc.contributor.authorHawa Abu-Bakar S.en_US
dc.contributor.authorFadilah Ab Aziz N.en_US
dc.contributor.authorBani N.A.en_US
dc.contributor.authorNabil Muhtazaruddin M.en_US
dc.contributor.authorid55330007200en_US
dc.contributor.authorid36634597400en_US
dc.contributor.authorid57200961058en_US
dc.contributor.authorid57203987646en_US
dc.contributor.authorid57203978437en_US
dc.contributor.authorid57203985173en_US
dc.contributor.authorid57189337505en_US
dc.contributor.authorid55578437800en_US
dc.date.accessioned2023-05-29T06:51:16Z
dc.date.available2023-05-29T06:51:16Z
dc.date.issued2018
dc.descriptionDefects; Linear regression; Neural networks; Regression analysis; Insulation defects; Multiple linear regressions; Multiple regressions; Offline; Partial discharge sources; Pd detections; PD measurements; Training and testing; Partial dischargesen_US
dc.description.abstractThis paper compares the capabilities of the artificial neural network (ANN) and multiple linear regression (MLR) for recognizing and discriminating partial discharge (PD) defects. Statistical fingerprints obtained from a several PD measurement were applied for training and testing both the ANN and MLR. The result indicates that for both the ANN and MLR trained and tested with the same insulation defect, the ANN has better recognition capability. But, when both ANN and MLR were trained and tested with different PD defects, the MLR is generally more sensitive in discriminating them. In this paper, the results were evaluated for practical PD recognition and it shows that both of them can be used simultaneously for both online and offline PD detection. � 2017 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8448033
dc.identifier.doi10.1109/IEEEGCC.2017.8448033
dc.identifier.scopus2-s2.0-85053906096
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85053906096&doi=10.1109%2fIEEEGCC.2017.8448033&partnerID=40&md5=f6ee6806b8481b4a1956a1ed10175660
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23725
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitle2017 9th IEEE-GCC Conference and Exhibition, GCCCE 2017
dc.titleComparison of artificial neural network and multiple regression for partial discharge sources recognitionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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