基于db4小波变换与BP神经网络的暂态电能质量扰动分类方法

Classification of Transient Power Quality Disturbances Based on db4 Wavelet Transform and BP Neural Network

电能质量扰动信号的分类识别是进行电能质量扰动分析和治理的重要前提。提出一种应用小波变换与神经网络相结合的暂态电能质量扰动分类方法。首先,针对暂态电能质量扰动信号的特点,选择db4小波变换来获得各层上的能量值,以提取不同扰动信号的特征参量。再通过确定适当的BP神经网络模型,对输入的扰动特征参量进行分类识别。仿真结果表明,该方法可以有效区分几种暂态电能质量扰动事件,且识别率较高。

The classification and identification of power quality disturbance signal is an important prerequisite for power quality disturbance analysis and control.This paper presents a classification method of transient power quality disturbances based on wavelet transform and neural network.Firstly,according to the characteristics of transient power quality disturbance signal,db4 wavelet transform is selected to obtain the energy value of each layer so as to extract the characteristic parameters of different disturbance signals.Secondly,by determining the appropriate BP neural network model,the input disturbance characteristics are classified and identified.Simulation results show that the method can distinguish several kinds of transient power quality disturbance events effectively and has achieved high recognition ratio.