基于DWT-PNN的柔性直流输电系统故障检测方法

柔性直流输电系统故障极大地影响了电力系统的稳定性.现有输电线路故障检测方法存在阈值选取困难、对过渡电阻阻值变化敏感、检测时间长等问题.提出一种基于小波能量占比的使用概率型神经网络(PNN)进行故障类型检测与位置判别的方法.通过对不同故障类型下的母线和线路测量电压进行快速傅里叶分析得出暂态电压频率特性,再利用离散小波变换(DWT)求得不同尺度下的小波能量特性,通过大量的离线仿真数据对PNN进行训练,根据PNN的输出结果实现故障类型与故障位置的精确判定.在PSCAD/EMTDC仿真环境下搭建了四端柔性直流电网电磁暂态模型进行仿真验证,结果表明所提方法可以准确地对高阻接地故障的故障类型与位置判别进行检测,不受过渡电阻阻值影响.

DWT-PNN based fault detection method for flexible DC transmission system

The failure of the flexible DC transmission system greatly affects the stability of power system. The existing transmission line fault detection methods have the problems of difficult threshold selection, sensitivity to transition resistance changes and long detection time. A method of fault type detection and position discrimination based on wavelet energy ratio using PNN(Probabilistic Neural Network) is proposed. The frequency characteristics of the transient voltage are obtained by fast Fourier analysis of the measured voltages of the bus and line under different fault types, and then DWT(Discrete Wavelet Transform) is used to obtain the wavelet energy characteristics at different scales. The fault type and fault location can be determined accurately according to the output results of PNN. The electromagnetic transient model of the four-terminal flexible DC transmission network is built under PSCAD/EMTDC environment. The simulative results show that the proposed method can detect the fault type and fault location of high resistance grounding fault accurately, without being affected by the transition resistance.