基于改进GSA-SVM算法的电能质量扰动分类方法

针对不同类型电能质量扰动信号分类准确率不高的问题，通过MATLAB/simulink搭建常见的9种不同的电能质量扰动信号的模型进行仿真分析，提出一种改进的万有引力搜索算法(improved gravitational search algorithm, IGSA)对支持向量机(support vector machine, SVM)的惩罚因子和核函数参数进行寻优的方法，通过优化SVM的惩罚因子和核函数参数，构建IGSA-SVM分类器，再把提取到的特征向量进行归一化之后输入到所构造好IGSA-SVM分类器中进行训练与分类。仿真结果表明，IGSA-SVM分类器的分类准确率比SVM和GSA-SVM这2种分类器都要好，可以实现对9种不同的电能质量扰动信号的快速准确分类，有利于解决实际的工程问题。

The classification method for power quality disturbance based on improved GSA-SVM algorithm

Aiming at the problem that the classification accuracy of different types of power quality disturbance signals is not high,nine different models of power quality disturbance signals are built by MATLAB / simulink to make simulation analysis,and a kind of optimization method that an improved gravitational search algorithm(IGSA)optimize the penalty factor and kernel function parameters of support vector machine(SVM)is proposed.By optimizing the penalty factor and kernel function parameters of SVM,the IGSA-SVM classifier is constructed,and then the extracted feature vectors are normalized and input into the constructed IGSA-SVM classifier for training and classification. The simulation results show that the classification accuracy of IGSA-SVM classifier is better than that of SVM and GSA-SVM. It can realize the fast and accurate classification of 9 different power quality disturbance signals,which is helpful to solve practical engineering problems.