Supraharmonics monitoring based on VSSESP-DBP dynamic compressed sensing algorithm

Introduction With the advancement of power electronic devices toward intelligent high-frequency operation and the widespread integration of distributed renewable energy sources, electrical power quality issues, particularly those arising from superharmonics, are becoming increasingly significant. The non-stationary and wide-frequency characteristics of superharmonic signals pose significant challenges for effective monitoring. Traditional static time-window-based methods struggle to accurately sample these non-smooth signals, leading to reconstruction errors and inefficiencies. Therefore, this study proposes a novel supraharmonics monitoring approach based on the VSSESP-DBP dynamic compressed sensing algorithm to enhance monitoring accuracy and efficiency. Methods To address the limitations of static time-window-based sampling, a dynamic time window with flexible modulation of window width is introduced. This modulation is achieved through a scale stretch factor, reducing reconstruction error. The study leverages the sparsity of superharmonic signals within the time window and proves the applicability of compressed sensing theory for dynamic compressive sampling. At the reconstruction end, the VSSESP-DBP dynamic reconstruction algorithm is designed. The variable step-size sparsity self-estimating subspace tracking (VSSESP) algorithm is employed to find the initial solution, while the dynamic basis tracking (DBP) algorithm exploits the time dependence of the signal support set. By using the solution from the previous moment as a priori information, the proposed method enhances reconstruction speed and efficiency. Results Experimental results demonstrate that the proposed method enables dynamic monitoring and reconstruction of superharmonics with reduced sampling data. The introduction of dynamic time windows significantly improves reconstruction accuracy compared to traditional methods. Furthermore, the VSSESP-DBP algorithm exhibits superior computational efficiency and real-time performance, effectively addressing the limitations of conventional approaches in continuous signal reconstruction. Discussion The proposed approach successfully mitigates challenges associated with non-stationary and wide-frequency superharmonic signal monitoring. The combination of dynamic time-window sampling and VSSESP-DBP-based reconstruction enhances both accuracy and computational efficiency. These findings highlight the potential of the method for real-time power quality monitoring applications. Future research could focus on optimizing algorithm parameters for different grid conditions and extending the approach to broader power quality disturbances.