# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
###### Abstract
[PERSON] proposed a fault line selection method grounded in a modified artificial bee colony optimization deep neural network (ACB-DNN), which could reduce training time [14]. [PERSON] et al. improved the complete ensemble empirical mode decomposition adaptive noise algorithm (CEEM-DAN) to decompose the zero-sequence current of each line into a series of intrinsic mode functions, converted it into image form, and then processed it through wavelet transform and used a convolutional neural network to output the fault probability, so as to locate the fault line [15]. Nonetheless, data-driven deep learning methodologies are inherently susceptible to the constraints imposed by the quantity and caliber of their training samples. Insufficient sample numbers or failure to meet requisite quality standards can impinge upon the ultimate accuracy of the models' predictions. In order to solve the problem of deep learning requiring a large amount of training data, transfer learning has gradually attracted the attention of scholars [16; 17; 18]. [PERSON] et al. used the pre-trained VGG-16 as the backbone model, and then fine-tuned the model using less than 900 public chest CT images to achieve the identification of COVID-19 patients [16]. Through a large number of experiments, [PERSON]. [PERSON] et al. found that fine-tuned pre-trained convolutional neural network models can effectively classify thermal images captured by switchgear units and achieve higher accuracy [17]. [PERSON] et al. proposed a continuous domain distribution adversarial network (CDDAN) based on deep domain adversarial network, which can realize transfer learning to a continuous and multi-dimensional target domain, and verified the feasibility and superiority of the method on datasets generated based on twin-spool turbofan engines [18]. [PERSON] et al. used domain adaptive transfer learning to complete variable condition migration to achieve fault line selection in the target domain, but a large number of target domain samples were still needed in the migration process [19]. [PERSON] et al. extracted similar fault features from other substation instances and migrated these features to target substation instances with limited samples [20]. This method achievedhigher fault line selection accuracy than other methods in cases with small sample sizes, but this method was affected by the similarity between substation instances. To address the above problems, a fault line selection method without samples from the target distribution network is proposed, which centers on training the model based on domain knowledge. Specifically, the main contributions of this paper can be summarized as follows:  
1. A method based on moving average filter (MAF) and motif difference field (MDF) for converting time series into images is proposed. The method starts with a moving average denoising of the one-dimensional zero-sequence current signal and then encodes it into a two-dimensional image using the motif difference field. The color distributions in MDF images are more intuitive and contain richer information than time-series data, and the image data can also be used directly as input for deep learning-based visual models. 2. A faulty line selection model based on MCECA-CloFormer is proposed. The line selection model utilizes the MCECA module to fuse fault images from multiple measurement points and the CloFormer module with a two-branch structure to sense the difference between fault and non-fault feature maps. It has been demonstrated that the model can achieve fast and accurate fault line selection in target distribution networks under different operating conditions and line parameters. The rest of this paper is organized as follows: Section 2 briefly analyzes the single-phase ground fault characteristics; Section 3 presents the MAF-MDF based imaging method; Section 4 introduces the MCECA-CloFormer based line selection model; the validity of the proposed method is verified and discussed in Section 5; and finally, Section 6 presents conclusions and future directions.