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A classification method of power quality disturbance based on wavelet packet decomposition

机译:基于小波包分解的电能质量扰动分类方法

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The power quality problems has been given more attention due to the proliferation of sensitive equipments. In order to mitigate the influence, various power quality disturbances must be classified before an appropriate action can be taken. Wavelet packet is developed on wavelet transform, which can provide more time-frequency information. This paper selects the energy and entropy of terminal nodes through wavelet packet decomposition as a feature vector respectively, using the Fisher linear classifier to design piecewise linear classifier in order to classify the disturbances, which are simulated and analyzed. The simulation results indicate that the entropy feature vector has a higher recognition accurate ratio.
机译:由于敏感设备的激增,电能质量问题受到了更多关注。为了减轻影响,必须对各种电能质量干扰进行分类,然后才能采取适当的措施。小波包是在小波变换的基础上开发的,可以提供更多的时频信息。本文通过小波包分解分别选择终端节点的能量和熵作为特征向量,利用Fisher线性分类器设计分段线性分类器对干扰进行分类,并进行了仿真分析。仿真结果表明,熵特征向量具有较高的识别准确率。

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