基于小波包分解的电能质量扰动分类方法
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摘要
随着敏感性设备的大量应用,电能质量问题已日益受到关注。对各种电能质量扰动进行分类是采取适当措施降低扰动带来影响的前提。小波包是在小波变换的基础上发展起来的,能够提供更为丰富的时频信息。文章分别选取小波包分解终节点的能量和熵作为特征矢量,应用Fisher线性分类器设计了分段线性分类器,对扰动分类进行了仿真识别。仿真结果表明,以熵为特征矢量的分类方法有较高的识别正确率。
Along with the wide application of sensitive equipments more and more attentions are paid to power quality. Classifying various disturbances to power quality is the premise of adopting appropriate measures to reduce the influences brought by disturbances. On the basis of wavelet transform the wavelet packet is developed, it can offer plentiful time-frequency information. Here, choosing the energy and entropy of terminal nodes through wavelet packet decomposition as feature vectors respectively and applying Fisher linear classifier, the piecewise linear classifier is designed and the simulation and analysis of disturbance classification are carried out. The simulation results show that the classification method, in which the entropy is used as feature vector, possesses higher classification correctness.
Along with the wide application of sensitive equipments more and more attentions are paid to power quality. Classifying various disturbances to power quality is the premise of adopting appropriate measures to reduce the influences brought by disturbances. On the basis of wavelet transform the wavelet packet is developed, it can offer plentiful time-frequency information. Here, choosing the energy and entropy of terminal nodes through wavelet packet decomposition as feature vectors respectively and applying Fisher linear classifier, the piecewise linear classifier is designed and the simulation and analysis of disturbance classification are carried out. The simulation results show that the classification method, in which the entropy is used as feature vector, possesses higher classification correctness.
引文
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[2] Gauda AM, Kanoun S H, Salama M M A et al. Wavelet-based signal processing for disturbance classification and measurement[J]. IEE Proceedings-Generation, Transmission and Distribution, 2002,149(3) : 310-318．
[3] LiGY, Zhou M, Zhang Z Y. Power quality disturbance automatic recognition based on wavelet and genetic network[C]. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, Beijing, China, 2002: 1923-1926．
[4] Ghosh A K, Lubkeman D L. The classification of power system disturbance waveforms using a neural network approach[J]. IEEE Transactions on Power Delivery, 1995, 10(1) : 109-115．
[5] 张智远,李庚银,冯任卿.基于小波和进化网络的电能质量动态扰动自动识别[J].华北电力大学学报,2002,29(3) :1-4． Zhang Zhiyuan, LiGengyin, FengRenqing, Auto recognition of power quality disturbance based on wavelet and genetic Net [J]. Journal of North China Electric Power University, 2002, 29(3) : 1-4．
[6] 彭玉华.小波变换与工程应用[M].北京:科学出版社,1999．
[7] 薛蕙,杨仁刚,罗红,等.采用小波变换分析配电网电能质量扰动[J].电网技术,2003,27(7) :60-65． XueHui, Yang Rengang, Luo Hong et al. Analysis of power quality disturbance in distribution network by wavelet transform[J]. Power System Technology, 2003, 27(7) : 60-65．
[8] 文继锋,刘沛.一种电能质量扰动检测的新方法[J].中国电机工程学报,2002,22(12) :17-20． Wen Jifeng, Liu Pei. A new method for detection of power qualitydisturbances[J]. Proceedings of the CSEE, 2002, 22(12) : 17-20．
[9] Antonini G, Orlandi A. Wavelet packet-based EMI signal processingand source identification[J]. TREE Transactions on ElectromagneticCompatibility, 2001, 43(2) : 140-148．
[10] Yen G G, Lin K C. Wavelet packet feature extraction for vibrationmonitoring[J]. IEEE Transactions on Industrial Electronics, 2000,47(3) : 650-667．
[11] 胡昌华,张君波,夏军,等.基于MATLAB的系统分析与设计一一小波分析[M].西安:西安电子科技大学出版社,1999．
[12] 王碧泉,陈祖荫.模式识别--理论、方法和应用[M].北京:地震出版社,1989．
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