一种基于PSO-SVM的电能质量扰动识别与分类的新方法

杨宁霞; 孙晧

杨宁霞,孙晧.一种基于PSO-SVM的电能质量扰动识别与分类的新方法[J].电测与仪表,2014,51(16):.
YANG Ning-xia,SUN Hao.A New Method of Power Quality Disturbance IdentificationBased on the PSO- SVM[J].Electrical Measurement & Instrumentation,2014,51(16):.

一种基于PSO-SVM的电能质量扰动识别与分类的新方法

A New Method of Power Quality Disturbance IdentificationBased on the PSO- SVM

DOI：

中文关键词: 电能质量扰动 SVM分类器 PSO 复小波变换电能质量监测

英文关键词:power quality disturbance, the classifier parameter of SVM,PSO, complex wavelet transform,power quality monitoring

基金项目:

作者中文名	作者英文名	单位
杨宁霞	YANG Ning-xia	山东科技大学电气与自动化工程学院
孙晧	SUN Hao	山东科技大学电气与自动化工程学院

摘要点击次数: 1509

中文摘要:

针对目前电网电能质量扰动识别与分类中采用的SVM分类器参数难以选择的问题,提出了一种基于粒子群(PSO)优化SVM的电能质量扰动识别新方法。利用MATLAB软件对实际电网中常见5种扰动信号进行建模,将检测到的电压信号经复小波变换后作为PSO-SVM的输入样本进行训练和测试。仿真结果表明,该方法能够快速、可靠地对电能质量扰动进行识别与分类,对电网的电能质量监测具有极高应用价值。

英文摘要:

Aiming at the current problem of difficult to choice the classifier parameter of SVM for the power quality disturbance identification and classification, put forward a new kind of power quality disturbance identification method based on the particle swarm optimization (PSO) SVM. Using MATLAB software to model five kinds of common power quality disturbance signal, the detected voltage signals were used as the input samples of the PSO-SVM training and testing after complex wavelet transform. The simulation results show that the method can identify and classify the power quality disturbance quickly and reliably, it has a high application value for power quality monitoring of the power grid.

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