基于LMD能量熵和GK模糊聚类的电能质量扰动识别

Power Quality Disturbance Identification Based on LMD Energy Entropy and GK Fuzzy Clustering

提出一种基于局部均值分解方法（LMD）能量熵和GK模糊聚类相结合的电能质量扰动识别的新方法。LMD能量熵具有局域化的特性并且能够表征扰动信号复杂度，可以量化扰动特征。GK模糊聚类可处理分布不规则数据，因此可对各种扰动信号进行识别。非平稳的扰动信号首先由LMD分解，得到若干个有物理意义的乘积函数（PF），通过Shannon熵的特征筛选方法对PF分量进行筛选，求取其能量熵组成特征向量。进而通过GK聚类对特征向量进行识别分类。实验表明，该方法能够有效准确地识别电能扰动信号，并具有良好的抗噪性。

A new approach for power quality disturbance identification based on local mean decomposition （LMD） energy entropy and GK fuzzy clustering is introduced. LMD energy entropy has localized features and can represent the complexity of disturbance signals, quantizing disturbance characteristic. GK fuzzy clustering can process data with irregular distribution, so as to identify various disturbance signals. Non-stationary disturbance signals are decomposed by LMD, obtaining a number of product function （PF） components with physical meaning, which is screened out by Shannon entropy feature selection methods to obtain the energy entropies and construct the eigenvectors. The constructed eigenvectors are further put into GK classifier to recognize different identification types. The experiment demonstrated that the method is able to identify disturbance signals accurately, with better anti-noise performance.