| 引用本文: | 肖白,李道明,穆钢,高文瑞,董光德.基于SA-PSO算法优化CNN的电能质量扰动分类模型[J].电力自动化设备,2024,44(5):185-190. |
| XIAO Bai,LI Daoming,MU Gang,GAO Wenrui,DONG Guangde.Power quality disturbance classification model based on CNN optimized by SA-PSO algorithm[J].Electric Power Automation Equipment,2024,44(5):185-190. |
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| 摘要: |
| 针对传统电能质量扰动分类模型中扰动特征复杂、识别步骤繁琐的问题，提出了一种通过模拟退火(SA)算法与粒子群优化(PSO)算法相结合来优化卷积神经网络(CNN)的电能质量扰动分类模型。将CNN卷积层中的二维卷积核替换成一维卷积核；采用SA算法对PSO算法进行改进，规避PSO算法陷入局部最优的困境；采用改进后的PSO算法对CNN进行参数寻优；利用优化CNN提取和筛选合适的特征，根据这些特征利用分类器得到最终分类结果。通过算例分析得出，使用基于SA-PSO算法优化的CNN的电能质量扰动分类模型能精确地识别出电能质量扰动信号。 |
| 关键词: 电能质量 扰动分类 卷积神经网络 粒子群优化算法 模拟退火算法 特征提取 |
| DOI：10.16081/j.epae.202312015 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(51177009)；国家重点研发计划项目(2017YFB0902205)；国网重庆市电力公司科技项目(SGCQDK00DWJS2100205) |
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| Power quality disturbance classification model based on CNN optimized by SA-PSO algorithm |
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XIAO Bai1, LI Daoming1, MU Gang1, GAO Wenrui1, DONG Guangde2
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1.Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China;2.State Grid Chongqing Electric Power Research Institute, Chongqing 401123, China
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| Abstract: |
| 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. |
| Key words: power quality disturbance classification convolution neural network particle swarm optimization algorithm simulated annealing algorithm feature extraction |
