基于CEEMDAN与小波自适应阈值的去噪新方法

一维电能质量信号中通常含有不同程度的白噪声,高效去噪是对电能质量信号进行检测与识别的重要前提。为了能有效地消噪并完整还原信号的奇异点等真实信息,提出了基于自适应白噪声的完备总体经验模态分解(CEEMDAN)与小波自适应阈值的去噪新方法。首先通过自相关法对CEEMDAN分解得到的含噪高频固有模态函数(IMFs)进行筛分；然后对这些高频分量进行小波自适应阈值降噪,这样就保留了高频部分的有效信息；最后与低频IMFs进行信号重构。仿真结果表明该方法去噪效果好,有效地保留了高频成分中的真实信息,为电能质量信号去噪提供了新思路。

A new method of combined denoising based on CEEMDAN and wavelet adaptive thresholding

One-dimensional power quality signal usually contains different degrees of white noise, and the efficient denoising is an important prerequisite for the detection and identification of power quality signals. In order to effectively eliminate the noise and restore the singular points of the signal, a new denoising method based on Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) and wavelet adaptive threshold is adopted. Firstly, the self-correlation method is used to sieve the high-frequency Intrinsic Mode Functions (IMFs) with noise decomposed by CEEMDAN. Then, wavelet adaptive threshold denoising is performed on the high frequency components, which preserves the effective information of the high frequencies. Finally, signal reconstruction is performed with other IMF components. The simulation results show that the proposed method has a good denoising effect and can effectively keep the real information in the high frequency components, which provides a new idea for the effective denoising of the power quality signal.