Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning  

作　　者：Xiuyong Yu Jun Cao Zhong Fan Mingming Xu Liye Xiao 

机构地区：[1]the Institute of Electrical Engineering and the Key Laboratory of Applied Superconductivity,Chinese Academy of Sciences,Beijing 100190,China [2]the University of Chinese Academy of Sciences(UCAS),Beijing 100049,China [3]Keele University,Keele,ST55GB,UK [4]the Electric Power Research Institute of State Grid Henan Electric Power Company,Zhengzhou 450000,China

出　　处：《CSEE Journal of Power and Energy Systems》2023年第6期2168-2178,共11页中国电机工程学会电力与能源系统学报（英文）

基　　金：the Key Program of the Chinese Academy of Sciences under Grant QYZDJ-SSW-JSC025;in part by the National Natural Science Foundation of China under Grant 51721005,and in part by the Chinese Scholarship Council(CSC).

摘　　要：Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.

关 键 词：Deep-learning method faulty feederc identification full-scale test field(FSTF) resonant groundingc distribution network single line to ground fault transfer learning 

分 类 号：TM862[电气工程—高电压与绝缘技术]