基于高阶累积量和自组织映射的电能质量暂态幅频分类

J. J. González de la Rosa, A. Aguera Perez, J. C. Palomares Salas, A. Moreno-Muñoz
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引用次数: 1

摘要

本文研究了电能质量(PQ)暂态的自动分类,根据其幅值和频率,并遵循由高阶统计测量建立的几何模式。聚类是通过与电异常相关的三阶和四阶特征实现的,而电异常又与50 hz电力线耦合。这篇论文的主要贡献是一个新的发现,即这些高阶累积量的最大值和最小值根据一组曲线分布,每一个曲线都与瞬态频率有关。给定一个统计阶数,曲线上的每个点对应于一个给定的瞬态初始振幅,以及统计估计量的两个极值。通过每条曲线的随机分组揭示了先验的隐藏几何,与底层现象联系在一起。一旦找到几何图形,我们展示了基于自组织映射的计算智能模量,它沿着每个频率曲线执行令人满意的学习。展示了具有两种不同几何形状的六神经元网络的性能。该经验是对PQ事件自动分类程序研究的延续。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Amplitude-frequency classification of Power Quality transients using higher-order cumulants and Self-Organizing Maps
This paper deals with the automatic classification of Power Quality (PQ) transients according to their amplitudes and frequencies, and following the geometrical pattern established via higher-order statistical measurements. The clustering is achieved thanks to the third and fourth-order features associated to the electrical anomalies, which in turn are coupled to the 50-Hz power-line. The main contribution of the paper is the novel finding that the maxima and the minima of these higher-order cumulants distribute according to a family of curves, each of which associated to the transient's frequency. Given a statistical order, each point in a curve corresponds to a given initial amplitude of a transient, and to a couple of extreme values of the statistical estimator. The random grouping through each curve reveals the a priori hidden geometry, linked to the subjacent phenomenon. Once the geometry has been found, we show the computational intelligence modulus, based in Self-Organizing Maps, which performs satisfactory learning along each frequency curve. Performance of a six-neuron network with two different geometries is shown. The experience is a continuation of the research towards an automatic procedure for PQ event classification.
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