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Artificial Intelligence Applications in the Diagnosis of Power Transformer Incipient Faults

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dc.contributor.authorWang, Zhenyuanen
dc.contributor.committeechairLiu, Yiluen
dc.contributor.committeememberNussbaum, Maury A.en
dc.contributor.committeememberDe La Ree, Jaimeen
dc.contributor.committeememberBroadwater, Robert P.en
dc.contributor.committeememberVanLandingham, Hugh F.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:14:59Zen
dc.date.adate2000-08-23en
dc.date.available2014-03-14T20:14:59Zen
dc.date.issued2000-08-08en
dc.date.rdate2001-08-23en
dc.date.sdate2000-08-10en
dc.description.abstractThis dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution. Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented. Several methods of power transformer incipient fault location were investigated, and a 7Ã 21Ã 5 MLP network was identified as the best choice. Several methods for on-load tap changer (OLTC) coking diagnosis were also investigated, and a MLP based modular network was identified as the best choice. Logistic regression analysis was identified as a good auditor in neural network input pattern selection processes. The above results can help developing better power transformer maintenance strategies, and serve as the basis of on-line DGA transformer monitors.en
dc.description.degreePh. D.en
dc.identifier.otheretd-08102000-21510032en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08102000-21510032/en
dc.identifier.urihttp://hdl.handle.net/10919/28594en
dc.publisherVirginia Techen
dc.relation.haspartChapter8.pdfen
dc.relation.haspartChapter7.pdfen
dc.relation.haspartChapter6.pdfen
dc.relation.haspartChapter5.pdfen
dc.relation.haspartChapter4.pdfen
dc.relation.haspartChapter3.pdfen
dc.relation.haspartChapter2.pdfen
dc.relation.haspartChapter1.pdfen
dc.relation.haspartChapter0.pdfen
dc.relation.haspartChapter9.pdfen
dc.relation.haspartReference.pdfen
dc.relation.haspartAppendix.pdfen
dc.relation.haspartChapter04.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPower Transformeren
dc.subjectFault Diagnosisen
dc.subjectArtificial Intelligenceen
dc.subjectDissolved Gas-in-oil Analysis (DGA)en
dc.subjectNeural Networken
dc.subjectFuzzy Systemen
dc.subjectExpert Systemen
dc.titleArtificial Intelligence Applications in the Diagnosis of Power Transformer Incipient Faultsen
dc.typeDissertationen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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