基于小波包去噪与EMD的故障电弧检测算法研究

经验模态分解(Empirical Mode Decomposition,简称EMD)方法是一种被广泛应用于故障诊断领域的信号处理方法,但是分解的结果容易受到高频噪声的干扰。因此本文提出利用小波包去除噪声,同时利用EMD分解进行故障电弧信号处理的研究方法。首先根据国标GB/T 31143-2014搭建故障电弧实验平台,采集故障电弧信号,利用小波包去除信号中的噪声,然后利用EMD方法对正常电弧和故障电弧进行分解得到一系列本征模态分量,通过计算求出这些分量的能量熵并进行归一化处理得到特征向量,最后进行支持向量机(SVM)训练,进而诊断得出故障电弧的故障类型。实验结果表明该方法可以有效准确地对故障电弧进行判断。

Research on fault arc detection algorithm based on wavelet packet noise reduction and EMD decomposition

Empirical mode decomposition (EMD) is a signal processing method widely used in the field of fault diagnosis.It has been successfully applied in the field of fault diagnosis,But the decomposition results are easily interfered by high frequency noise.Therefore, this paper puts forward the method of using the wavelet packet to remove the noise and the fault arc signal processing by EMD decomposition.First,the fault arc experimental platform is built according to the national standard GB/T31143-2014,and the fault arc signal is collected,using wavelet packet to remove the noise in the signal, Then the EMD method is used to decompose the normal arc and the fault arc to obtain a series of IMFs,the energy entropy of these components is obtained by calculation and the eigenvectors are obtained by normalization,finally,support vector machine (SVM) training, and then the fault type arc fault diagnosis.The experimental results show that the method can effectively and accurately determine the fault arc.