Intelligent Identification Method of Power System Transient Fault Events Based on Machine Learning
In order to accurately identify transient fault types in different fault locations,fault moments and noisy environments,an intelligent identification method of transient fault events in power system based on machine learning is proposed.The eigen-values of transient fault structure are used as the input vectors of the quantum particle swarm optimization and radial basis func-tion neural network model,the optimal parameters of the optimized radial basis function neural network are used as output by selecting the appropriate parameter coding strategy,fitness function and termination conditions,the system then completes the intelligent identification of fault events.The simulation results show that the optimal training parameters can be obtained by u-sing quantum particle swarm optimization(QPSO)to optimize radial basis function(RBF)neural network.The training time is 3.561 s and the training error is 0.000 257 7.This method can correctly identify the transient fault type in different fault loca-tion,fault time and noise environment,and the identification efficiency is significant.
万方数据
维普
