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Improvement of wavelet based methods for classification of power quality disturbances

机译:基于小波的电能质量扰动分类方法的改进

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In this paper an improvement of wavelet based methods for detection and classification of power quality disturbances is presented. In the feature extraction process wavelet analysis is also used as in the comparing methods. However, the feature vector is extended with three other coefficients in order to improve the accuracy of the algorithm. In order to evaluate the proposed method, large number of experiments is performed, using SVM (Support Vector Machines) as a classification method. The obtained classification accuracy is higher than 98%.
机译:本文提出了一种基于小波的电能质量扰动检测和分类方法的改进。在特征提取过程中,小波分析也用作比较方法。然而,为了提高算法的准确性,特征向量被扩展了其他三个系数。为了评估提出的方法,使用SVM(支持向量机)作为分类方法进行了大量实验。获得的分类精度高于98%。

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