Diagnosis of DC series arc fault based on multiple wavelet transform and optimal fractional wavelet energy entropy

Abstract

In the DC distribution system, the propagation of arc noise can interfere with normal lines, and accurate and timely diagnosis of the location of series arc fault (SAF) is a challenging problem. In this article, a SAF diagnosis method is proposed from a system perspective, which can accurately identify the fault line. First, multiple wavelet transform is used to decompose the currents of different lines, and the fractional wavelet energy entropy is extracted to construct the feature vector. Then, random forest is employed to analyze the importance of features and to select the optimal features. Finally, a kernel extreme learning machine can fuse the features and output the diagnosis results. The offline experimental results indicate that the proposed method has a diagnosis accuracy of 99.82%, which is higher than those of nine comparison methods, and the effectiveness and advancement of the proposed method are verified. The online experimental results show that the proposed method can diagnose SAF within 110 ms, and the diagnosis speed is able to satisfy the requirements of UL1699B. Moreover, under transient conditions, the proposed method can effectively avoid false alarms and maintain stability.