Intelligent Electrical Fault Detection and Recognition Based on GWO-SVM Model
To address the issue of low accuracy in traditional classification algorithms for distinguishing electrical faults,a Grey Wolf Optimization-Support Vector Machine(GWO-SVM)model is proposed to improve electrical fault diagnosis accuracy.Firstly,waveform signals of commonly encountered linear and nonlinear household elec-trical appliances such as incandescent lamps and microwave ovens in normal operation and during arc faults are col-lected from real-life scenarios.Secondly,frequency domain features are extracted from these signals.Finally,the GWO algorithm optimizes the Support Vector Machine,and the performance of GWO-SVM is compared with unoptimized SVM and BP neural networks.The GWO-SVM model achieves an accuracy of 90％.
electrical faultsintelligent detectiongrey wolf optimization algorithmsupport vector machine
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