An artificial neural network approach to transformer fault diagnosis

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dc.contributor.authorZhang, Yuwenen
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T21:43:08Zen
dc.date.adate2008-08-22en
dc.date.available2014-03-14T21:43:08Zen
dc.date.issued1996en
dc.date.rdate2008-08-22en
dc.date.sdate2008-08-22en
dc.description.abstractThis thesis presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. The goal of the research is to investigate the available transformer incipient fault diagnosis methods and then develop an ANN approach for this purpose. This ANN classifier should not only be able to detect the fault type, but also should be able to judge the cellulosic material breakdown. This classifier should also be able to accommodate more than one type of fault. This thesis describes a two-step ANN method that is used to detect faults with or without cellulose involved. Utilizing a feedforward artificial neural network, the classifier was trained with back-propagation, using training samples collected from different failed transformers. It is shown in the thesis that such a neural-net based approach can yield a high diagnosis accuracy. Several possible design alternatives and comparisons are also addressed in the thesis. The final system has been successfully tested, exhibiting a classification accuracy of 95% for major fault type and 90% for cellulose breakdown.en
dc.description.degreeMaster of Scienceen
dc.format.extentviii, 73 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-08222008-063051en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08222008-063051/en
dc.identifier.urihttp://hdl.handle.net/10919/44322en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1996.Z436.pdfen
dc.relation.isformatofOCLC# 35615396en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttransformeren
dc.subjectartificial neural networken
dc.subjectdissolved gas analysis (DGA)en
dc.subjectfault detection and diagnosisen
dc.subject.lccLD5655.V855 1996.Z436en
dc.titleAn artificial neural network approach to transformer fault diagnosisen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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