基于小波神经网络的特高压直流输电线路故障测距方法  

Fault Location Method for Ultra-High Voltage Direct Current Transmission Lines Based on Wavelet Neural Networks

作　　者：郭星[1] GUO Xing(State Grid Shanxi Electric Power Company,Taiyaun 030021,China)

机构地区：[1]国网山西省电力公司,山西太原030021

出　　处：《中国水能及电气化》2024年第10期37-41,共5页China Water Power & Electrification

摘　　要：传统特高压直流输电线路故障测距方法直接对故障区段进行识别,未对故障电压信号空间状态模型进行构建,这导致了传统方法测距的精度存在较大误差。针对这一问题,文章提出了基于小波神经网络的特高压直流输电线路故障测距方法。通过构建故障电压信号空间状态模型进行故障区段识别,最后基于小波神经网络实现故障测距。对比实验结果表明,本文研究方法测距精度误差较小,具有重要的实际应用价值。Traditional fault location methods for ultra-high voltage direct current transmission lines directly identify the fault section without constructing a spatial state model of the fault voltage signal,which leads to significant errors in the location accuracy.In response to this issue,this paper proposes a fault location method for ultra-high voltage direct current transmission lines based on wavelet neural networks.The method involves constructing a spatial state model of the fault voltage signal to identify the fault section,followed by fault location using the wavelet neural network.Comparative experimental results show that the proposed method has a smaller location accuracy error,demonstrating significant practical application value.

关 键 词：小波神经网络 特高压直流输电 电线路故障 故障测距 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]