@article{Wang2026, 
author = {Xiaowei Wang and Chenjing Li and Zhenfeng Liang and Liang Guo and Weibo Liu and Huan Du},
title = {Fault Feeder Detection Method of Distribution Network Based on Gramian Angular Field and Convolutional Neural Network},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {2},
pages = {749-767},
keywords = {Convolutional neural network, fault feeder detection, gramian angular field, zero sequence current},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2022.00080},
doi = {10.17775/CSEEJPES.2022.00080},
abstract = {Due to compensation function of arc suppression coil, grounding current of distribution network is weak and protection is not easy to operate. In addition, traditional feeder detection method is not suitable for weak fault feature extraction. In order to improve accuracy and universality of fault feeder detection in distribution network, it established a mixed database containing three completely different topology models, and proposed a novel fault feeder detection method based on gramian angular field (GAF) and convolutional neural network (CNN). First, it used GAF to transform zero sequence current under different fault conditions, therefore, it obtained a large number of current characteristic images. Then, it trained CNN by mixed sample database including characteristic images. After determining CNN structure and parameters, CNN model is used to judge whether feeder is faulty or not. Finally, proposed method is verified by simulation data and field test data, and it has certain robustness to some disturbances, such as noise, data loss, asynchronous sampling, data proportion change, arc grounding fault occurrence.}
}