Research on Signal Denoising of Pumped-Storage Units Based on Parameter-Adaptive VMD and Wavelet Thresholding

To address the non-stationary and non-linear characteristics of vibration signals collected by sensors in pumped-storage units, as well as their susceptibility to strong background noise interference, this paper proposes a joint signal denoising method combining parameter-adaptive Variational Mode Decomposition (VMD) and wavelet thresholding. First, the Improved Particle Swarm Optimization (IPSO) algorithm is utilized to adaptively optimize the key parameters of VMD using a comprehensive fitness function as the objective, thereby achieving the optimal decomposition of the signal. Subsequently, a cross-correlation analysis method is introduced to screen the decomposed components, followed by a secondary denoising process using a wavelet threshold to accomplish the final signal denoising. Experimental validations using simulated run-out signals and field-measured sensor data from a pumped-storage power station, along with comparisons against other methods, demonstrate that the proposed method can eliminate noise more effectively. It significantly improves the signal-to-noise ratio (SNR) and reduces the root mean square error (RMSE). Consequently, this study provides a reliable data foundation for the subsequent research and analysis of the units, demonstrating substantial practical engineering significance.