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基于小波变换和二叉树SVM的电能质量扰动分类
发表时间：2010-05-31 浏览量：1705 下载量：542
| 全部作者： | 冯浩，周雒维，刘毅 |
| 作者单位： | 基于小波变换和二叉树SVM的电能质量扰动分类 |
| 摘 要： | 电能质量扰动分类的关键在于特征提取和分类器设计，对此，提出了一种基于小波变换和二叉树结构支持向量机（support vector machine, SVM）实现电能质量扰动分类的方法。首先，通过交流暂态仿真软件(alternative transients program, ATP)产生8种典型扰动信号和2种复合扰动信号作为样本集；然后，通过小波变换进行多个特征的提取，包括模极大值个数、信号在特定频带下的能量和小波系数标准差，这样不仅减小了数据量，而且更好地反映了扰动信号的局部特征；最后，通过样本集对二叉树结构SVM分类器进行训练和测试。测试结果表明：该方法能够有效识别常见的10种扰动信号，具有分类正确率高、训练时间短的优点。 |
| 关 键 词： | 电力系统；电能质量；扰动分类；小波变换；支持向量机 |
| Title： | Power quality disturbances classification based on wavelet transform and binary tree architecture SVM |
| Author： | FENG Hao, ZHOU Luowei, LIU Yi |
| Organization： | College of Electrical Engineering, Chongqing University |
| Abstract： | Classification of power quality (PQ) disturbances has two key processes: feature extraction and classifier design. A new method based on wavelet transform and support vector machine with binary tree architecture (SVM�BTA) is presented for power quality disturbances classification. At first, by use of alternative transients program (ATP), eight typical and two complex disturbance signals are generated as sample set. Then wavelet transform is applied to obtain multiple features, including the number of modulus maximum value, signal energy and the standard deviation of wavelet coefficients in special frequency band. This can reduce the data size and reflect the local feature of disturbance signals better. At last, the SVM�BTA multi�classifier is trained and tested by the sample set. The test results show that the proposed method can effectively classify ten disturbance signals with high classification accuracy and short training time. |
| Key words： | power system; power quality; disturbance classification; wavelet transform; support vector machine |
| 发表期数： | 2010年5月第10期 |
| 引用格式： | 冯浩，周雒维，刘毅. 基于小波变换和二叉树SVM的电能质量扰动分类[J]. 中国科技论文在线精品论文，2010，3（10）：1002-1008. |
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