基于粒子群优化与卷积神经网络的电能质量扰动分类方法  

Power Quality Disturbance Classification Method Based on Particle Swarm Optimization and Convolutional Neural Network

作　　者：董光德 李道明 陈咏涛 马兴 付昂 穆钢[2] 肖白[2] DONG Guangde;LI Daoming;CHEN Yongtao;MA Xing;FU Ang;MU Gang;XIAO Bai(Electric Power Research Institute,State Grid Chongqing Electric Power Company,Yubei District,Chongqing 401123,China;School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,Jilin Province,China)

机构地区：[1]国网重庆市电力公司电力科学研究院,重庆市渝北区401123 [2]东北电力大学电气工程学院,吉林省吉林市132000

出　　处：《发电技术》2023年第1期136-142,共7页Power Generation Technology

基　　金：国网重庆市电力公司科技项目(SGCQDK00DWJS2100205)。

摘　　要：针对传统电能质量扰动分类方法中人工选取特征困难、步骤繁琐和分类准确率低等问题,提出了一种基于粒子群优化(particle swarm optimization,PSO)算法与卷积神经网络(convolutional neural network,CNN)的扰动分类方法。首先,利用reshape函数将各电能质量扰动信号的一维时间序列分别转成行列相等的二维矩阵,并对这些二维矩阵进行适当划分,形成训练数据集和测试数据集;其次,基于CNN构建电能质量扰动的分类模型;再次,采用PSO算法对该分类模型的参数进行优化,使用训练数据集对优化后的电能质量扰动分类模型进行训练;最后,使用测试数据集对经过训练的电能质量扰动分类模型进行测试,根据输出标签得到各类电能质量扰动的分类结果。仿真结果表明:该分类模型可以自行提取电能质量扰动数据的特征,相较于其他电能质量扰动分类模型,其对电能质量扰动信号的分类准确率更高。Aiming at the problems of difficult manual selection of features,cumbersome classification steps and low accuracy in traditional power quality disturbance classification methods,a disturbance classification method based on particle swarm optimization(PSO)and convolutional neural network(CNN)was proposed.Firstly,the one-dimensional time series of power quality disturbance signals were converted into two-dimensional matrices with equal rows and columns by using the reshaping function,and these two-dimensional matrices were properly divided into training data set and test data set.Secondly,the classification model of power quality disturbance was built based on CNN.Thirdly,the PSO algorithm was used to optimize the parameters of the classification model,and the trained data set was used to train the optimized power quality disturbance classification model.Finally,the trained power quality disturbance classification model was tested by using the test data set,and the class results of various power quality disturbances were obtained according to the output labels.Simulation results show that the classification model can extract the characteristics of power quality disturbance data by itself.Compared with other power quality disturbance classification models,this method has higher classification accuracy for power quality disturbance signals.

关 键 词：新能源 电能质量 扰动分类 特征提取 粒子群优化(PSO) 深度学习 卷积神经网络(CNN) 

分 类 号：TK715[动力工程及工程热物理—流体机械及工程]