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Power Quality Disturbance Recognition Based on Fitting Redundant Lifting Wavelet Packet and Energy Analysis

机译:基于拟合冗余提升小波包和能量分析的电能质量扰动识别

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To realize accurate detection and recognition of power quality disturbance, a fitting redundant lifting wavelet packet method combined with energy analysis is proposed in this paper. As six new wavelets obtained through data fitting and lifting algorithm are employed for disturbance analysis, two issues i.e. selection of both the wavelet and the node signal are well investigated, while the energy analysis is utilized to distinguish the type of power quality disturbance. Simulation results show that distinct selection of wavelet or node signal will leave different impact on disturbance detection, and with energy analysis two types of power quality disturbance i.e. the voltage swell and voltage sag can be easily and precisely recognized.
机译:为了实现电能质量扰动的准确检测和识别,提出了一种结合能量分析的拟合冗余提升小波包方法。由于通过数据拟合和提升算法获得了六个新的小波进行扰动分析,因此很好地研究了两个问题,即小波和节点信号的选择,同时利用能量分析来区分电能质量扰动的类型。仿真结果表明,小波或节点信号的不同选择将对扰动检测产生不同的影响,并且通过能量分析,可以容易且精确地识别出两种类型的电能质量扰动,即电压骤升和电压骤降。

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