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

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.