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首页> 外文会议>MELECON 2010;IEEE Mediterranean Electrotechnical Conference >Classification of two common power quality disturbances using wavelet based SVM
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Classification of two common power quality disturbances using wavelet based SVM

机译：基于小波支持向量机的两种常见电能质量扰动分类

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摘要

Development of technology increased the attention of the research community on power quality (PQ) disturbance classification problem. This paper presents wavelet based effective feature extraction method and support vector machines (SVM) for PQ disturbance classification problem. Two common kinds of power quality disturbances, voltage sag and swell, are considered in this paper. After multi-resolution signal decomposition of PQ disturbances, feature vector can be obtained. Multi-resolution analysis (MRA) technique of discrete wavelet technique (DWT) and Parseval's theorem are employed to extract the energy distribution features of sag and swell signals. SVM are used to classify these feature vectors of PQ disturbances. Performance of two kinds of method used in SVM is compared aspect of training time and training error.
机译：技术的发展引起了研究界对电能质量（PQ）干扰分类问题的关注。本文提出了基于小波的有效特征提取方法和支持向量机（SVM），用于PQ干扰分类问题。本文考虑了两种常见的电能质量扰动，即电压骤降和骤升。在对PQ干扰进行多分辨率信号分解后，可以获得特征向量。利用离散小波技术（DWT）的多分辨率分析（MRA）技术和Parseval定理来提取垂度和膨胀信号的能量分布特征。 SVM用于对PQ干扰的这些特征向量进行分类。比较了支持向量机中两种方法的性能在训练时间和训练误差方面。

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