基于变分模态分解的暂态扰动波形去噪算法

针对经验模态分解去噪时存在的模态混叠问题，提出一种变分模态分解与滑动均值滤波相结合的去噪算法。首先通过寻找变分模型最优解将含噪信号分解成若干个固有模态。然后利用相关系数准则确定最优分解层数K以及其对应的相关模态，并用滑动均值滤波器对非相关模态进行处理以得到其中的有用分量。最后基于相关模态和非相关模态中提取的有用分量构造去噪后的信号。仿真表明，与经验模态分解去噪和小波去噪相比，所提出的算法能够在更有效去除暂态扰动中噪声的同时，保留暂态扰动中的特征信息。

Transient disturbance waveform denoising algorithm based on variational mode decomposition

There is a problem of mode mixing in empirical mode decomposition (EMD) denoising. Thus a denoising algorithm combining variational mode decomposition and moving mean filtering is proposed. First, the noise signal is decomposed into several inherent modes by finding the optimal solution of the variational model. Then, the optimal decomposition layer K and its corresponding correlated modes are determined by the correlation coefficient criterion, and the uncorrelated modes are processed by the moving mean filter to get the useful components. Finally, the denoised signal is constructed based on the useful components extracted from the correlated and uncorrelated modes. Simulation results show that compared with EMD denoising and wavelet denoising, the proposed algorithm can effectively remove the noise in the transient disturbance while retaining the characteristic information in the transient disturbance. This work is supported by the Science and Technology Project of the Headquarters of State Grid Corporation of China (No. 52130421000S).