基于GWO-TCN网络的HVDC输电线路故障诊断  

Fault diagnosis of HVDC transmission lines based on GWO-TCN networks

作　　者：刘辉[1] 李永康 张淼 刘维 Liu Hui;Li Yongkang;Zhang Miao;Liu Wei(Hubei Power Grid Intelligent Control and Equipment Engineering Technology Research Center,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学湖北省电网智能控制与装备工程技术研究中心,武汉430068

出　　处：《电子测量技术》2021年第22期168-174,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(61903129)项目资助。

摘　　要：现有高压直流(HVDC)故障检测方法灵敏度低,难以识别高阻接地故障,提出了一种基于改进灰狼算法(GWO)优化时间卷积神经网络(TCN)的HVDC传输系统故障检测方法,整流侧检测装置采集的故障电流信号直接用作TCN的输入数据,克服了故障信号处理的繁琐过程。利用Simulink仿真软件建立±500 kV高压直流输电线路模型,对不同故障区域和故障类型进行仿真实验,使用基于LSTM模型,BiLSTM模型和CNN模型3种模型的故障检测方法进行比较。测试结果表明,GWO-TCN网络能够可靠、准确地在过渡电阻高达800Ω时进行HVDC输电线路故障选极和选区。The existing high voltage direct current(HVDC) fault detection methods have low sensitivity and are difficult to identify high resistance grounding faults. This paper proposes a HVDC transmission system fault detection method based on improved grey wolf optimizer(GWO) optimized time convolutional network(TCN), The fault current signal collected by the rectifier side detection device is directly used as the input data of TCN, which overcomes the cumbersome process of fault signal processing. The ± 500 kV HVDC transmission line model is established by using Simulink simulation software, and the simulation experiments are carried out for different fault areas and fault types. The fault detection methods based on LSTM model, BILSTM model and CNN model are compared. The test results show that gwo-tcn network can reliably and accurately select the fault pole and selection of HVDC transmission line when the transition resistance is up to 800 Ω.

关 键 词：时间卷积神经网络 灰狼优化算法 故障识别 高压直流输电 

分 类 号：TM712[电气工程—电力系统及自动化]