Fault Line Detection Using Waveform Fusion and One-dimensional Convolutional Neural Network in Resonant Grounding Distribution Systems

作     者：Jianhong Gao Moufa Guo Duan-Yu Chen Jianhong Gao;Moufa Guo;Duan-Yu Chen

作者机构：College of Electrical Engineering and AutomationFuzhou UniversityFuzhou 350108China Department of Electrical EngineeringYuan Ze UniversityTaoyuan 32003TaiwanChina 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2021年第7卷第2期

页      面：250-260页

核心收录：

学科分类：0808[工学-电气工程] 080803[工学-高电压与绝缘技术] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China through the Project of Research of Flexible and Adaptive Arc-Suppression Method for Single-Phase Grounding Fault in Distribution Networks(No.51677030) 

主　　题：Fault line detection one-dimensional convolutional neural network resonant grounding distribution systems waveform fusion 

摘      要：Effective features are essential for fault *** to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution *** paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform *** compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault ***,the 1-D CNN output is used to update the value of the counter in order to identify the fault *** the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line *** addition,the proposed method owns fault-tolerant *** if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D *** results verified that the proposed method can work effectively under various fault conditions.