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Analysis of power quality disturbance signal based on improved compressed sensing reconstruction algorithm

Compressed sensing is a novel theory which combined signal sampling and compression together, and this paper propose an improved reconstruction algorithm. Firstly this paper analysis the sparsity of the power quality disturbance signal and the selection of the measurement matrix, then, this paper proposes an improved regularization sparsity adaptive matching pursuit algorithm (RCoSaMP) on the basis of analyzing and summarizing the existing greedy reconstruction algorithms. The improved reconstruction algorithm combines the advantages of CoSaMP and SAMP, it can adaptively adjusts the step size by signal agent and backtracking thought even if the sparsity of original signal is unknown, then, exactly reconstructs the original signal with a small amount of sampled data. MATLAB simulation results show that in the reconstruction of power quality disturbance signals such as harmonics and voltage sag, the improved reconstruction algorithm is superior to other greedy algorithms in terms of reconstruction speed and accuracy.