An Integrated DC Series Arc Fault Detection Method for Different Operating Conditions

Series arc fault (SAF) has severe impacts on the safety of dc power supply systems. Timely and accurate SAF detection under different operating conditions is an open and challenging problem. To address this problem, this article proposes an integrated SAF detection method for different operating conditions. In the proposed method, dual-tree complex wavelet transform (DT-CWT) is employed to obtain an accurate current signal decomposition. The singular values of each wavelet component are then extracted by using an improved matrix construction method, which can effectively reduce the computational cost of constructing the high-dimension features. Finally, the kernel extreme learning machine (KELM) is applied to fuse the feature information for SAF detection. A series of experiments are presented to demonstrate the effectiveness of the proposed method. The results of offline experiment show that the accuracy of the proposed method has higher detection accuracy than that of the six state-of-the-art methods under different operating conditions. In this article, the proposed method is then embedded into the hardware of the experimental platform for online in-service implementation. The online experimental results show that the proposed method achieves fast and accurate SAF detection and, at the same time, offers outstanding reliability and stability in system dynamic transients.