Power quality disturbance classification model based on CNN optimized by SA-PSO algorithm

被引：0

作者：

Xiao B. ^{[1
]}

Li D. ^{[1
]}

Mu G. ^{[1
]}

Gao W. ^{[1
]}

Dong G. ^{[2
]}

机构：

[1] Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin

[2] State Grid Chongqing Electric Power Research Institute, Chongqing

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 05期

基金：

中国国家自然科学基金;

关键词：

convolution neural network; disturbance classification; feature extraction; particle swarm optimization algorithm; power quality; simulated annealing algorithm;

D O I：

10.16081/j.epae.202312015

中图分类号：

学科分类号：

摘要：

Aiming at the problems of complex disturbance features and complicated recognition steps in traditional power quality disturbance classification models，a power quality disturbance classification model based on convolutional neural network（CNN） optimized by combining simulated annealing（SA） algorithm and particle swarm optimization（PSO） algorithm is proposed. The two-dimensional convolution kernel in the CNN convolution layer is replaced by one-dimensional convolution kernel. The SA algorithm is used to improve the PSO algorithm to avoid the PSO algorithm falling into the local optimal dilemma. Then，the improved PSO algorithm is used to optimize the parameters of CNN. The improved CNN is used to extract and screen appropriate features，according to which，the final classification results are obtained by the classifier. Through the example analysis，it is concluded that the power quality disturbance classification model based on CNN optimized by SA-PSO algorithm can accurately identify the power quality disturbance signal. © 2024 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：185 / 190

页数：5

共 21 条

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