电网技术2023，Vol.47Issue(12)：5169-5180,12.DOI:10.13335/j.1000-3673.pst.2022.2061
基于人工神经网络和网络迁移的双端输电线路非同步故障测距算法
Asynchronous Fault Location Algorithm for Two-terminal Transmission Lines Based on Artificial Neural Network and Network Migration
摘要
Abstract
The current algorithms of the two-terminal transmission line fault location rely either on the line models and the line parameters,or on the large amounts of historical fault data.To solve this problem,first by summarizing the fault location principles of the distributed parameter model and theΠ-type model,this paper establishes the fault location function of the two-terminal transmission line according to the positive sequence voltage and the positive sequence current before and after a fault.Then,an asynchronous fault location algorithm is proposed for the two-terminal transmission lines based on the artificial neural network and the network migration but without depending on the line model and the line parameters.Finally,a simulation model of the 500k V transmission line is built on the Matlab/Simulink,verifying the correctness and reliability of the proposed algorithm.The theoretical analysis and simulation results show that the proposed algorithm has higher accuracy without being affected by the factors such as the fault locations,the fault types,the transition resistance,the fault initial phase angles,the load current and the asynchronous data.In addition,by making full use of a large amount of normal data and a small amount of fault data of the lines based on the idea of the network migration,the proposed algorithm still has a higher accuracy when the line parameters change within the range of less than±1％.关键词
双端输电线路/故障测距/人工神经网络/网络迁移/非同步数据Key words
two-terminal transmission lines/fault location/artificial neural network/network migration/asynchronous data分类
信息技术与安全科学引用本文复制引用
陈晓龙,孙丽蓉,李永丽,李斌..基于人工神经网络和网络迁移的双端输电线路非同步故障测距算法[J].电网技术,2023,47(12):5169-5180,12.基金项目
国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U2066210).Project Supported by National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(U2066210). （U2066210）
