基于压缩感知的三相电能质量扰动信号压缩及分类新方法

New compression and classification method of three-phase power quality disturbance signal based on compressed sensing

首先,选取傅立叶基作为压缩感知中的过完备字典、高斯随机矩阵作为观测矩阵、正交匹配追踪(OMP)算法作为重构算法,对电能质量扰动信号进行压缩采样。然后,采用灰度共生矩阵纹理特征中的能量特征值、灰度值出现概率两种方法对压缩感知重构信号进行分类检测。试验结果表明:本文方法可以同时实现对三相电能质量信号的压缩重构,也可以实现对信号的准确分类,并减少了信号压缩采样过程中的数据量。

First,in order to reconstruct three-phase power quality disturbance signals,the Fourier basis is chosen as the over-complete dictionary;Gaussian random matrices as the observation matrix;and orthogonal pursuit algorithm as the reconstruction algorithm.Then,the energy eigenvalue of gray level cooccurrence matrix texture features and the probability of the occurrence of grey value are used to classify the disturbance signals.The experiment results show that the proposed method can not only realize simultaneous compression and reconstruction of three-phase electric signals and accurate classification of such signals,but also reconstruct the original signal samples,reducing the data volume of signal compression and sampling.