Study of fault location algorithm for distribution network with distributed generation based on IGA-RBF neural network

Abstract

The access of the Distributed Generation (DG) has to make the fault location problem of distribution network extremely complex and affect the efficiency and accuracy of fault location. According to the fault information that the Supervisory Control And Data Acquisition (SCADA) of distribution network upload and considering the change of configuration and logic relation of distribution network protection after DGs access, this paper proposed a radial basis function (RBF) neural network based on Improved Genetic Algorithm (IGA), which used the real-coded genetic algorithm with adaptive crossover and mutation into the gradient-dropping algorithm as the RBF network learning algorithm, and constructed a new switch function and fitness function. And then the improved algorithm was applied to the fault location of distribution network containing distributed power supply. The simulation results show that the RBF neural network based on IGA not only has the advantages of simple structure and fast operation, but also has better generalization performance. The analysis and comparison results show that the optimized Improved algorithm can effectively improve the convergence speed and precision, and it has good fault-tolerance to the lack or distortion of the fault information.