【摘要】 为实现对脉冲暂态和振荡暂态2类暂态电能质量扰动的分类,提出一种基于高阶累积量的暂态电能质量扰动Mahalanobis距离分类法.该方法利用高阶累积量提取暂态扰动的高阶统计特征,选取其2,3,4阶累积量最大值和最小值构成扰动信号的6维特征向量,计算测试样本特征向量与标准模板之间的Mahalanobis距离,以"距离最小"作为分类判据,实现对2类暂态电能质量扰动的分类.仿真结果表明,低阶累积量不能单独用于区分不同类型暂态电能质量扰动;特征向量维数越高,对分类过程越有利.所提方法分类原理简单、准确率高,是暂态电能质量扰动的有效分类方法.更多还原

【Abstract】 A novel classification and recognition algorithm for transient power quality disturbance signals,which is based on high order accumulation and Mahalanobis distance,was proposed to complete the classification of impulse transient disturbances and oscillation transient disturbances in this paper.The proposed method extracted high order statistical characteristics of transient disturbances with high order accumulation and selected the maximum and the minimum of 2-order,3-order and 4-order accumulation as the 6D feature vector of disturbances.Then the Mahalanobis distance between feature vector of test sample and the standard template was calculated.The classification of two kinds of disturbances was completed with the calculation results according to the minimum distance criterion.Simulation results showed that low order accumulation could not be used independently in the classification of transient disturbances.The higher the dimension of the feature vector is,the more efficient the classification progress is.The principle of the proposed method is simple.It is an effective method to classify transient power quality disturbances with high accuracy.更多还原