基于二维离散平稳小波的电能质量扰动分类

针对电能质量扰动分类这一难题，提出一种基于二维离散平稳小波的分类方法。首先对信号进行一层二维小波变换，得到一个低频分量和水平、垂直和斜线3个高频分量，利用这4个部分的信号能量组成特征向量，再通过水平高频系数的模极大值将稳态和暂态扰动分开，分别建立稳态和暂态神经网络实现分类。该方法只需要采用最简单的小波函数db1对信号进行一层小波变换，对噪声不敏感，简单易行。仿真结果表明了该方法的有效性。

Power Quality Disturbance Classification Based on 2-D Static Wavelet Transform

To classify power quality disturbances, a method using 2-dimensional discrete static wavelet transform is introduced. First, the technique of 2-dimensional wavelet transform (WT) is employed to extract the energy distribution features of the distorted signal at three different high-frequency and one low-frequency. Then, the probabilistic neural network (PNN) classifies these extracted features to identify the disturbance type according to the energy features and module maximum value at the horizontal high-frequency. It is convenient for using the best simple wavelet function such as db1 and decomposing one-level WT. The simulation results show the effectiveness of the proposed method.