# 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. fast fault line selection; motif difference field; MCECA-CloFormer; fault processing time; arc high-resistance grounding faults; active distribution networks +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
into the Duffing system for fault line selection [8]. But during signal reconstruction, there may be signal reconstruction errors, resulting in distortion of the original signal, which would further affect the subsequent fault line selection process. [PERSON] et al. employed fast Fourier transform (FFT) to analyze the transient zero-sequence current to determine the decomposition scale of variational modal decomposition (VMD), then performed VMD and combined it with the energy method to quantitatively describe the decomposition results to achieve fault line selection [9]. Notwithstanding, the FFT method has the problem of spectrum leakage. Some scholars have combined machine learning to achieve fault line selection [10; 11]. [PERSON] et al. extracted pure transient electrical quantities based on the improved Hilbert-Huang transform (IHHT), constructed feature vectors by combining standard deviation, energy entropy and amplitude distortion, and established the fault line selection model through a random forest classifier, transforming the fault line selection problem into a binary classification problem and realizing automatic identification of fault lines [10]. [PERSON] et al. used ensemble empirical mode decomposition to decompose zero-sequence currents before and after faults into five kinds of IMFs with different components, and obtained the energy entropy of transient zero-sequence currents via Hilbert transformation. Then, the feature vectors were input to STOA-SVM for training to achieve fault line selection [11]. However, this method is affected by the parameter selection of the sooty term optimization algorithm. In general, the above methods all require feature extraction of signals, and the result of this feature extraction directly affects the accuracy of line selection, so the robustness of these methods is poor. In recent years, deep learning methods have been extensively studied due to their ability to significantly reduce the accumulation of errors inherent in manual feature extraction processes, coupled with their end-to-end advantages and strong generalization capabilities [12]. [PERSON] et al. concatenated the transient zero-sequence currents, measured at both ends of the line section after a single-line-to-ground fault occurrence by the digital fault indicator, to construct characteristic waveforms. Thereafter, they employed a one-dimensional convolutional neural network (1-D CNN) to adaptively extract features from these waveforms, facilitating the localization of the faulty section [13].