An Identification Method of Fault Type Based on GWO-SVM for Distribution Network

To achieve the accurate and fast identification of fault type for distribution network, a fault type identification method based on grey wolf optimization algorithm (GWO) and support vector machine (SVM) is proposed in this paper. When faults occur, the zero-sequence voltage of fault signals are obtained, and then the low-frequency energy values of them are calculated. Then the wavelet singular entropy value is calculated by Singular value decomposition (SVD) method and entropy theory. The low-frequency energy values and singular entropy values are used as the input of GWO-SVM classifier to identify the fault type. The results of simulations show that the proposed method can identify all grounding faults and short-circuit faults correctly, and the prediction accuracy is better than it of SVM classifier.