Fa-Mb-ResNet for Grounding Fault Identification and Line Selection in the Distribution Networks

Accurate and fast identification of the fault types and the fault feeders can improve the distribution networks' power supply reliability. This article focuses on two issues of classifiers in performing fault identification and line selection of the distribution networks, namely, the low utilization rate of fault information and the insufficient accuracy. We propose to use multilabel and multiclassification and build a fast-multibranch residual network (Fa-Mb-ResNet) to accomplish the identification and line selection of the distribution network grounding fault simultaneously. Our work has the following contributions. First, we propose a method of frequency division and time division for learning the features of the time-frequency matrix based on wavelet transformation. Second, we propose an improved residual unit (IRU) structure, which employs different small branches and convolution kernels to achieve the fusion of abstract fault feature information in different dimensions and enhance learning efficiency. Finally, the IRU structure is connected end to end. The new approach fully exploits the side fault information. Our extensive experiments show that the Fa-Mb-ResNet is faster, more adaptable, and has better anti-interference than the state-of-the-art methods in fault identification and line selection of the distribution network.

Automated summary

This article proposes a method based on multilabel and multiclassification and a fast-multibranch residual network (Fa-Mb-ResNet) for simultaneous fault identification and line selection in distribution networks. The method uses frequency division and time division to learn the features of the time-frequency matrix and employs an improved residual unit structure to enhance learning efficiency. Experimental results show that the proposed method outperforms state-of-the-art methods in fault identification and line selection in distribution networks.