基于EMD连续几何分布的水电机组振动信号降噪

Denoising vibration signals from hydroelectric generating units using EMD-based consecutive geometric distribution similarity measure algorithm

考虑到水电机组在电力系统中更多的承担调峰、调频、备用等任务,开展复杂工况下的机组振动信号降噪算法研究对早期故障辨识和电网稳定运行意义重大。因此,本文提出了一种基于经验模态分解连续几何分布相似性的水电机组振动信号降噪算法。首先,对经验模态分解筛分得到的不同固有模态分量进行重构,并利用非参数核密度估计理论对不同分量重构信号的概率密度函数进行拟合。其次,引入豪斯多夫距离建立概率密度函数几何分布之间的相似性评价指标,并根据豪斯多夫距离的变化趋势实现水电机组振动故障信号分量与噪声分量之间的最优界定。最后通过仿真实验和工程实例对算法的可行性进行了验证。结果表明所提出算法对于低信噪比下的水电机组振动信号有着良好的降噪效果。

Given the fact that hydroelectric generating units are often used for peak and frequency modulation and spinning reserve, noise reduction of their vibration signals is dramatically significant to promoting the incipient fault identification and safe operation of power systems. This paper develops a novel EMD-based denoising algorithm using the similarity measure between consecutive geometric distributions. The signals was reconstructed by using different intrinsic mode functions generated from EMD sifting, and fitted the probability density functions of these reconstructed signals by the nonparametric kernel density estimation theory. Then, a Hausdorff distance was adopted to calculate the indexes for evaluating the similarity measure between the consecutive geometric distributions of probability density functions, and an optimal separation between characteristic IMF components and noisy IMF components is carried out through variation trend analysis of the similarity measure indexes. This method is validated using model simulations and engineering application, and the results demonstrate it achieves a remarkable effect on noise reduction of hydroelectric generating unit signals.