Series Arc Fault Detection Method Based on Multi-feature Fusion and SVM
For the problem that the existing traditional electrical protection device cannot effectively detect the series arc fault,a kind of series arc fault detection method based on multi-feature fusion and improved SVM is proposed in this paper.Firstly,the arc experiment is performed by setting up an arc fault platform,the current signal of typical loads during normal operation and arc faults are obtained;Then,the collected current signals are subjected to time domain,frequency domain and time-frequency domain analysis to construct series arc feature index set;Finally,the series arc feature index set is taken as the input vector of SVM,and the particle swarm algorithm is used to optimize the SVM so to improve the accuracy of the classification model.The test results show that the accuracy rate of series faults arc identification using the the method proposed in this paper reaches more than 95% .
series arc faultfault recognitionfeature extractionSVMPSO
万方数据
维普
