2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077) · 2002 · 11 citations · 12 references
ComputingComputer ScienceArtificial IntelligenceDiagnostic ReasoningMachine LearningDiagnosisDiagnosticsElectrical System DiagnosticsExpert SystemsFault DetectionFault DiagnosisDiagnostic Expert SystemsExpert Systems For DiagnosisExpert Systems For Fault DiagnosisFault IdentificationFault LocationLogistic RegressionLogistic Regression AnalysisNeural Network-based Diagnosis
An artificial neural network and expert system based diagnostic system for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA) has been developed. This system takes advantage of the inherent positive features of each method and offers a better diagnostic accuracy. The knowledge base of its expert system (EPS) is derived from IEEE and IEC DGA standards and expert experiences to include as many known diagnosis rules as possible. The topology and training data set of its artificial neural network (ANN) are carefully selected to extract known as well as unknown diagnostic rules implicitly. The combination of the ANN and EPS outputs has an optimization mechanism to ensure high diagnostic accuracy. This work has been reported in the past. In this paper, the new development in fault location identification using logistic regression analysis and neural network is introduced. Test results show that it is possible not only to diagnose and predict fault types, but also to predict the location of the fault.
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An Artificial Neural Network Approach to Transformer Fault Diagnosis
Yuxin Zhang, Xuewen Ding, Yike Liu et al. · IEEE Power Engineering Review · 1996 · 262 citations · Full text
Computing, Electrical Engineering, Artificial Intelligence +15