5G承载网下基于经验小波变换和卷积神经网络的配网故障诊断方法

配电网拓扑结构复杂、分支众多、潮流分布不平衡,且存在通信网络覆盖不完善问题,给精确故障诊断带来很大难度.首先,基于5G承载网络的分布式配电网故障诊断系统,提出了网络时延和丢包模型,测试了实际网络时延.其次,提出了基于经验小波变换(empirical wavelet transform,EWT)和卷积神经网络(convo...

EWT-CNN fault diagnosis method for distributed network under 5G network

The distribution network has complex topology, large amount of branches, unbalanced power flow distribution, and imperfect communication network coverage. These disadvantages bring great difficulty to accurate fault diagnosis. In this paper, a fault diagnosis system based on 5G bearer network is constructed, the network delay and packet loss model are built, and the time delay of the actual network system. Then the Empirical Wavelet Transform (EWT) and Convolutional neural network (CNN) is introduced to the fault diagnosis. The different frequency components are obtained through the EWT transmission of the recorded electric quantity. The probability neural network model is constructed for each component to form the fault diagnosis method of EWT-CNN distribution network, and the fault judgment results are given. The experimental result demonstrates the effectiveness of the proposed EWT-CNN fault diagnosis method for distributed grid under 5G bearer network.