Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

作     者：Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Houjie Tong;Robert C.Qiu;Dongxia Zhang;Haosen Yang;Qi Ding;Xin Shi

作者机构：Department of Electrical EngineeringCenter for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and CommunicationHuazhong University of Science and TechnologyWuhan 430000China Electric Power Research InstituteHaidian DistrictBeijing 100192China School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing 102206China 

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3期

页      面：456-471页

核心收录：

学科分类：080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 

基　　金：This work was supported by the National Key Research and Development Program of China under Grant 2018YFF0214704 

主　　题：Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information 

摘      要：We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.