Publication:
Analysis of partial discharge measurement data using a support vector machine

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dc.citedby18
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorHao L.en_US
dc.contributor.authorLewin P.L.en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid14030216100en_US
dc.contributor.authorid7102386669en_US
dc.date.accessioned2023-12-28T08:57:37Z
dc.date.available2023-12-28T08:57:37Z
dc.date.issued2007
dc.description.abstractThis paper investigates the recognition of partial discharge sources by using a statistical learning theory, Support Vector Machine (SVM). SVM provides a new approach to pattern classification and has been proven to be successful in fields such as image identification and face recognition. To apply SVM learning in partial discharge classification, data input is very important. The input should be able to fully represent different patterns in an effective way. The determination of features that describe the characteristics of partial discharge signals and the extraction of reliable information from the raw data are the key to acquiring valuable patterns of partial discharge signals. In this paper, data obtained from experiment is carried out in both time and frequency domain. By using appropriate combination of kernel functions and parameters, it is concluded that the frequency domain approach gives a better classification rate. �2007 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4451430
dc.identifier.doi10.1109/SCORED.2007.4451430
dc.identifier.scopus2-s2.0-50449096613
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-50449096613&doi=10.1109%2fSCORED.2007.4451430&partnerID=40&md5=c9ad86601ddbe6a823dba52b4d5a1e19
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29774
dc.sourceScopus
dc.sourcetitle2007 5th Student Conference on Research and Development, SCORED
dc.subjectOn-line monitoring
dc.subjectPartial discharge
dc.subjectPartial discharge classification
dc.subjectPattern recognition
dc.subjectSupport vector machine
dc.subjectClassification (of information)
dc.subjectDischarge (fluid mechanics)
dc.subjectElectric machine insulation
dc.subjectFace recognition
dc.subjectFeature extraction
dc.subjectFluid mechanics
dc.subjectFrequency domain analysis
dc.subjectImage retrieval
dc.subjectLearning systems
dc.subjectResearch and development management
dc.subjectVectors
dc.subjectOn-line monitoring
dc.subjectPartial discharge
dc.subjectPartial discharge classification
dc.subjectPattern recognition
dc.subjectSupport vector machine
dc.subjectPartial discharges
dc.titleAnalysis of partial discharge measurement data using a support vector machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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