Research on Power Quality Signal Reconstruction Method Based on Compressed Sensing
Xinyan Zhao, Chuanyang Liu · 2022
The phenomenon of power pollution is becoming more and more seriously, on account of the theory of compressed sensing, it is of great significance to the research of power quality disturbance signal recognition. To solve the problem of the current power quality signal reconstruction algorithms are not adaptive and low reconstruction accuracy, the sparse adaptive algorithm is proposed to apply to the power quality signal reconstruction. DFT basis is selected as the sparse basis, observation matrix is designed as binary block diagonal matrix and SACoSaMP algorithm is proposed to reconstruct the power quality signal, which uses recursive to dynamically adjust the real sparsity of the approximation signal through the change of residual. The experimental results show that, the power quality signals are reconstructed using the proposed algorithm, the SNR of all the reconstructed signals are higher than 32 dB, ERP are over 99.7%, and MSE are under 2.5%. The indexes of the proposed algorithm are obviously superior to the existing ROMP algorithm, which further verifies the superiority and applicability of the proposed algorithm.