A Power Quality Disturbance Denoising Method Based on Improved MVO-VMD and Period Clustering MSSA

Power quality is crucial to the stability of power systems, and an effective power quality disturbance denoising method can help maintain this stability. This paper proposes a joint denoising method that integrates an Improved Multiverse Optimizer (IMVO), Variational Mode Decomposition (VMD), and period clustering Multichannel Singular Spectrum Analysis (MSSA). First, the improved MVO algorithm is used to optimize the number of decomposition modes and the penalty factor of VMD, and a weighted evaluation index is employed to select components for signal reconstruction. Second, a period clustering algorithm is applied to segment the reconstructed signal into periodic slices. Finally, MSSA is utilized for noise reduction to obtain clean power quality signals. Simulation results demonstrate that the proposed method achieves a Signal-to-Noise Ratio (SNR) improvement of more than 8 dB and a Root Mean Square Error (RMSE) below 0.05 under noisy conditions. The denoising performance and stability of the proposed method are significantly superior to those of comparison algorithms, providing reliable data support for power quality disturbance detection.