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| 一种基于PSO-SVM的电能质量扰动识别与分类的新方法 | |
| 引用本文： | 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) |
| 作者姓名： | 杨宁霞 孙晧 |
| 作者单位： | 1. 山东科技大学电气与自动化工程学院,山东青岛,266590 2. 山东电力潍坊市供电公司,山东潍坊,261000 3. 山东电力新泰市供电公司,山东泰安,271200 |
| 基金项目： | 山东省自然科学基金资助项目，山东省科技发展计划资助项目，山东科技大学学生专利研究及申请资助项目 |
| 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的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较高的应用价值。 |
| 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 |
| 收稿时间： | 2013-12-11 |
| 修稿时间： | 2013-12-11 |
A New Method of Power Quality Disturbance IdentificationBased on the PSO- SVM |
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| YANG Ning-xia and SUN Hao. A New Method of Power Quality Disturbance IdentificationBased on the PSO- SVM[J]. Electrical Measurement & Instrumentation, 2014, 51(16) | |
| Authors: | YANG Ning-xia and SUN Hao |
| Affiliation: | College of Information and Electrical Engineering,Shandong University of Science and Technology,College of Information and Electrical Engineering,Shandong University of Science and Technology |
| Abstract: | 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. |
| Keywords: | power quality disturbance the classifier parameter of SVM PSO complex wavelet transform power quality monitoring |
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