VMD-PE协同SNN的输电线路故障辨识方法

Fault identification method of transmission lines based on VMD-PE and SNN

针对输电线路短路故障危害大、故障辨识率较低等问题,提出一种结合变分模态分解排列熵(VMD-PE)与孪生神经网络(SNN)的故障辨识方法,利用瞬时频率均值对VMD进行参数优化,确定分解层数K,通过VMD分解故障时的三相电压,计算分解后每个分量的排列熵,将其作为故障特征量;将故障特征输入到训练好的SNN中进行相似性度量,比较两个输入样本之间的相似程度,判别出输电线路短路故障类型。通过仿真实验验证了该方法的可行性,并与其他分类方法相对比,证明了该方法的准确性和优越性。

Aiming at the problems of damage caused by short-circuit faults and low fault identification rate of transmission lines,a fault identification method combining VMD-PE and siamese neural networks(SNN)is proposed.For determining the number of decomposition layers K,use the instantaneous frequency mean to optimize VMD parameters,decompose the three-phase voltage at fault by VMD,calculate the permutation entropy of each component after decomposition,and use them as the fault features;input the fault features into the trained SNN for similarity measurement,compare the similarity between the two input samples to determine the type of short-circuit fault on the transmission line.The feasibility of the method is verified by simulation experiments,and compared with other classification methods,the accuracy and superiority of the method are proved.