Abstract
AbstractThe 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.
Highlights
In recent years, with the increasing use of nonlinear source in the field of mechanical processing, some problems occur (Zu, Hao, Yang, & Qiu, 2013)
In order to overcome the disadvantage of Regularized Orthogonal Matching Pursuit (ROMP) algorithm and preserve the advantage of batch atom selection of packet matching pursuit algorithm, we propose a new algorithm which is based on ROMP algorithm but can achieve better performance in PQ analysis
Contrast experiments of different algorithms In order to verify the performances of the compressive sensing theory (CS) and the Dice-Regularized Orthogonal Matching Pursuit (D-ROMP) algorithm in the presence of harmonic signals, we have done a lot of contrast experiments for PQ analysis of one mechanical equipment which are shown from Figures 4–17
Summary
With the increasing use of nonlinear source in the field of mechanical processing, some problems occur (Zu, Hao, Yang, & Qiu, 2013). Compressed sensing is a new sampling theory, which can reconstruct the signal perfectly based on the signal sparse features, by using few measurement data with much smaller than Nyquist sampling rate. This paper will improve the compressive sensing algorithm and apply it to the mechanical equipment power system to gathering the harmonic data. Power quality monitoring system based on CS theory PQ information data of mechanical processing equipment are sampled sparsely by smart meters (Figure 1). These information data will become one sparse matrix and will be transmitted into the sever computer to be reconstructed and analyzed by the wireless network. It is obvious that it will significantly reduce the transmission amount of sample data and improve real time performance of the monitoring system
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