Computational intelligent Techniques for power transformer fault identification and classification using DGA

dc.contributor.guideRajaram, Men_US
dc.coverage.spatialInformation and Communication Engineeringen_US
dc.creator.researcherIndra, Getzy Daviden_US
dc.date.accessioned2014-09-08T10:05:49Z
dc.date.available2014-09-08T10:05:49Z
dc.date.awarded30-10-2012en_US
dc.date.completed01-10-2012en_US
dc.date.issued2014-09-08
dc.date.registeredn.d.en_US
dc.description.abstractThis research is a proposed methodology for diagnosing the faults newlinethat occur in a Power Transformer by analyzing the Dissolved Gas Analysis newlineDGA data taken from the oil immersed transformers The various intelligent newlinetechniques used for diagnosing the faults include Fuzzy Logic and Artificial newlineNeural Networks along with multi classifier systems and principal component newlineAnalysis The diagnosis of various faults is carried out using DGA with newlineInternational Electrotechnical Commission IEC Institute of Electrical and newlineElectronic Engineers IEEE standards which occur due to the thermal and newlineelectrical stresses inside the transformer oil A study of the literature related newlineto the area of research, spanning approximately 47 years showed that the newlinelimitations in decision making have improved year after year by way of newlinesubstituting new methods by the researchers newline newlineen_US
dc.description.notereference p.166-175en_US
dc.format.accompanyingmaterialNoneen_US
dc.format.dimensions23cm.en_US
dc.format.extentxvii, 177p.en_US
dc.identifier.urihttp://hdl.handle.net/10603/24751
dc.languageEnglishen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.relation-en_US
dc.rightsuniversityen_US
dc.source.universityUniversityen_US
dc.subject.keywordDissolved Gas Analysisen_US
dc.subject.keywordInformation and Communication engineeringen_US
dc.subject.keywordInstitute of Electrical and Electronic Engineersen_US
dc.subject.keywordInternational Electrotechnical Commissionen_US
dc.titleComputational intelligent Techniques for power transformer fault identification and classification using DGAen_US
dc.title.alternative-en_US
dc.type.degreePh.D.en_US

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