Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer

Abstract

When a single-phase grounding fault occurs in resonant ground distribution network, the fault characteristics are weak and it is difficult to detect the fault line. Therefore, a fast fault line selection method based on MCECA-CloFormer is proposed in this paper. Firstly, zero-sequence current signals were converted into images using the moving average filter method and motif difference field to construct fault data set. Then, the ECA module was modified to MCECA (MultiCNN-ECA) so that it can accept data input from multiple measurement points. Secondly, the lightweight model CloFormer was used in the back end of MCECA module to further perceive the feature map and complete the establishment of the line selection model. Finally, the line selection model was trained, and the information such as model weight was saved. The simulation results demonstrated that the pre-trained MCECA-CloFormer achieved a line selection accuracy of over 98% under 10 dB noise, with a remarkably low single fault processing time of approximately 0.04 s. Moreover, it exhibited suitability for arc high-resistance grounding faults, data-missing cases, neutral-point ungrounded systems, and active distribution networks. In addition, the method was still valid when tested with actual field recording data.