IEEE PES Transmission and Distribution Conference and Exhibition · 2003 · 14 citations · 13 references
Fault DiagnosisEngineeringMachine LearningFault ForecastingFault Detection TechniqueReliability EngineeringData ScienceData MiningPattern RecognitionFault AnalysisSystems EngineeringPower SystemsElectrical EngineeringFault LocationComputer EngineeringStructural Health MonitoringPower System ProtectionSignal ProcessingAutomatic Fault DetectionFault EstimationSmart GridFault DetectionArtificial Neural Network
This paper presents a hybrid method of a data precondition technique and an artificial neural network (ANN) for fault detection to estimate the fault location and the type in the transmission systems. FFT is used as a data precondition technique to extract the features of input variables. Also, the radial basis function network (RBFN) is employed to approximate a nonlinear relationship between input and output variables as ANN. To enhance the model accuracy, this paper proposes a new RBFN called D-RBFN that makes use of DA clustering in determining the center vector and the width of the radial basis function. The D-RBFN has a global structure obtained by global clustering. The proposed method is successfully applied to a sample system.
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