基于扩展频谱时域反射法的电缆故障类型识别改进算法仿真研究OA

Aiming at the problem that spread spectrum time delay reflectometry(SSTDR)technology is difficult to realize fault location and type classification synchronously in power cable fault online detection,this paper proposes a cable fault location and type identification system based on long short-term memory(LSTM)network and residual network(ResNet),namely LSTM-ResNet.Firstly,based on the cable traveling wave propagation theory,the propagation and reflection model of SSTDR detection signal is established,and the influence of impedance mutation on the time-frequency characteristics of reflected signal under different fault types is analyzed.Secondly,the cable fault simulation model is built by Matlab/Simulink platform,and the four typical working conditions of open circuit fault,low resistance fault,high resistance fault and local damp are simulated.The results show that the LSTM-ResNet model can achieve a fault classification accuracy of 99.38%on a test set containing 4000 samples.This method can provide new ideas for breaking through the limitations of traditional methods relying on artificial feature design,solving the problem of gradient degradation in deep network training and digital online detection of cable faults.