Multiple Faults Diagnosis of Distribution Network Lines Based on Convolution Neural Network with Fuzzy Optimization

摘要

With the continuous expansion of power grid, the system structure becomes more and more complex, and multiple faults occur frequently. And multiple faults are the key and difficulty of fault diagnosis. Due to the huge and complex power grid structure and the large data size of fault processing, it is necessary to diagnose the power grid fault quickly and accurately. In this paper, based on convolutional neural network, a multi-fault diagnosis model of distribution network based on fuzzy optimal convolutional neural network is proposed. Firstly, fault line and fault type judgment based on two soft maximum classifiers are analyzed. Membership functions of distribution network faults are established by using fuzzy theory. Secondly, the influence of convolution kernel number and sample width on the accuracy of model diagnosis is studied and analyzed. Simulation results show that, under the same conditions, the accuracy of fuzzy optimized convolutional neural network for multiple fault diagnosis is higher than that of convolutional neural network. The time of fault diagnosis and training is less than that of convolutional neural network.