基于增强VMD相关分析的水电机组摆度信号降噪

为有效提升强背景噪声与复杂电磁干扰下水电机组摆度信号的分析精度，研究提出了一种基于增强VMD相关分析的摆度信号降噪方法。首先对摆度信号构造Hankel矩阵并进行奇异值分解，进而基于均值滤波策略筛选有效奇异值，求得Hankel估计矩阵并反向重构信号，实现低频段信号增强；再利用变分模态分解将重构信号分解为系列模态分量，并分别进行自相关分析，求得归一化自相关函数及对应的能量集中度指标；最后基于能量集中度选择有效分量进行特征信号重构，最终得到降噪后的摆度信号。通过仿真分析与电站实测振摆信号降噪验证，证明了所提方法具有较好的降噪性能。

Denoising swing signals from hydro-electric generating units based on enhanced variational mode decomposition and correlation analysis

Based on enhanced variational mode decomposition and correlation analysis, this paper develops a novel denoising method for improving analytical accuracy of swing signals generated from hydro-electric generating units with strong noisy background and complicated electromagnetic interference. First, we construct the Hankel matrix of a swing signal and calculate its components via singular value decomposition. Then, we single out an effective singular value using the mean filtering strategy, obtain the Hankel estimation matrix, and inversely reconstruct the signal, thereby achieving an enhancement of those signals in the low band. The signal so reconstructed can be decomposed into a collection of mode components by variational mode decomposition, and thus using autocorrelation analysis, we can calculate the energy focusability indexes of the normalized autocorrelation functions for all its mode components. Finally, by these indexes the effective components are selected and aggregated into a denoised swing signal. Application to denoising the simulated signals and the swing signals measured from a hydro-electric generating unit shows that our method is capable of achieving a good denoising performance.