基于遗传算法优化支持向量机的电能质量暂态扰动识别新方法  

New Method of Transient Power Quality Disturbances Recognition Using SVM Optimized by Genetic Algorithm

作　　者：林琳[1] 彭华[2] 戚佳金 黄南天[2] 

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]东北电力大学电气工程学院,吉林吉林132012 [3]国网杭州供电公司,浙江杭州310009

出　　处：《水电能源科学》2016年第11期200-203,共4页Water Resources and Power

基　　金：国家自然科学基金项目(51307020);2016年吉林省科技发展计划项目(20160411003XH);吉林省社科基金项目(2015A2);吉林省教育厅"十三五"科技项目(吉教科合字[2016]第90号)

摘　　要：针对电能质量识别领域中,采用随机参数的支持向量机(SVM)分类器识别随机暂态扰动信号准确率低、优化耗时长等问题,提出一种基于遗传算法(GA)优化SVM识别电能质量暂态扰动(PQD)的新方法(GASVM)。首先,仿真生成具有随机噪声水平和扰动参数的9种PQD信号;接着,通过S变换,提取出6种信号特征构成输入特征向量,用于训练SVM分类器;再采用GA对SVM进行参数寻优,进而获得优化的GA-SVM分类器;最后,采用GA-SVM识别PQD信号。仿真对比试验表明,新方法能准确识别不同噪声环境下的9种PQD信号,分类准确率及优化所需时间均优于PSO优化SVM方法(PSO-SVM)。SVM classifier has low accuracy and long time consuming for identifying random disturbance signals in the field of power quality recognition.A new method of transient power quality identification is proposed based on the SVM optimized by genetic algorithm.Firstly,9type of PQD signals with random noise level and perturbation parameters are generated by simulation.Then,6type of signal features extracted through the S transform are constituted as the input vectors,which are used to train the SVM classifier.Furthermore,GA is applied to optimize the parameters of SVM and the optimized GA-SVM classifier is obtained.Finally,GA-SVM model is applied to identify PQD signals.The simulation experiments show that the new method is able to accurately identify 9type of PQD signals in different noise environments.The classification accuracy and the optimization time are better than that of the PSO-SVM method.

关 键 词：电能质量 随机噪声 S变换 GA SVM 参数优化 扰动识别 

分 类 号：TM714[电气工程—电力系统及自动化]