An improved reconstruction algorithm based on compressed sensing for power quality analysis

Abstract

The application and analysis of compressive sensing theory in power quality has been received more and more attention. Reconstruction algorithm is one of the most important contents of the compressive sensing theory, and as one of the reconstruction algorithms with its excellent reconstruction performance, the regularized Orthogonal Matching Pursuit algorithm is widely used. Based on the analysis of the Regularized Orthogonal Matching Pursuit (ROMP) algorithm, an improved Dice-Regularized Orthogonal Matching Pursuit algorithm is proposed. Use the idea of normalization to change the selection rule of element groups and use the Dice coefficient to calculate the similarity between elements and residuals, which can effectively improve the reconstruction performance of the algorithm. Simulation results show that the improved algorithm has better performance than the ROMP algorithm in each index, and the validity and reliability is proved.

Keywords

• compressive sensing theory (cs)
• regularized omp algorithm
• improved d-romp algorithm