Abstract: Using EMD method to denoise the low frequency oscillation signal of the power system with low signal-to-noise ratio, there will be a large error, affecting the identification accuracy of low-frequency oscillation signal. In order to solve these problems, an improved EMD denoising method is proposed in this paper. The   IMF modal components are obtained by EMD decomposition of the low frequency oscillation signal of the power system, the normalized autocorrelation functions are obtained and the demarcation point   between noise dominant mode and signal dominant mode is determined. Then, the noise dominant mode is denoised, and the denoised components are reconstructed with the signal dominant mode to obtain the power system low frequency oscillation signal. Finally, multi-signal Prony analysis of reconstructed signals is carried out to extract the characteristics of low-frequency oscillation in power system. The experimental results show that the improved EMD method is more effective for low frequency signal denoising with low signal-to-noise ratio (SNR), the improved EMD method and the multi-signal Prony algorithm are applied to improve the performance of the EMD multi-signal Prony algorithm. The characteristics of low-frequency oscillation signal in power system have the advantages of fast speed, high resolution and good fitting effect.

Key words:  Power systems, low frequency oscillation, denoising, EMD, autocorrelation function, Prony algorithm

摘要： 利用EMD方法对低信噪比的电力系统低频振荡信号进行去噪，会存在较大误差，影响低频振荡信号的辨识精度。为了解决这类问题，提出了一种改进的EMD去噪方法。通过对电力系统低频振荡信号进行EMD分解，得到 个IMF模态分量，分别求取归一化自相关函数，判断出噪声主导模态和信号主导模态的分界点 ，之后对噪声主导模态进行去噪，将去噪后的各分量同信号主导模态重构得到电力系统低频振荡信号，最后对重构信号进行多信号Prony分析，提取电力系统低频振荡特征。利用改进的EMD方法、改进EMD的多信号Prony对理想信号和仿真系统进行实验，结果表明改进的EMD方法对低信噪比的低频振荡去噪效果更为突出，结合改进EMD多信号Prony算法提取电力系统低频振荡信号特征，具有速度快、分辨率高、拟合效果好的优点。

关键词: 电力系统, 低频振荡, 去噪, EMD, 自相关函数, Prony算法