Compression And Reconstruction Method Research For Short-time Power Quality Disturbance Signals

As the research hotspot and difficulty,power quality affects safe and reliable operation of the power grid.To monitor and control power quality in real time,a large number of data sampling rate are required at a high sampling.The traditional power quality acquisition is based on the Nyquist theorem,resulting in a mass of sampled data and a heavy burden during the storage and transmission.Since the sparsity property in some domain,power quality signals can be sampled by the approach of compressed sensing(CS)at far below the Nyquist sampling rate without loss of information.However,the sparsity features of short-time power quality disturbance signals are not considered in the existing CS-based reconstruction methods.Thus we can further improve their reconstruction performance.To this end,a sparsity feature-based reconstruction method is proposed in this paper.Besides,to reduce hardware calculation and cost,a single-bit sampling and reconstruction method for short-time power quality disturbance signals is also proposed.The main research in this paper is as follows:(1)According to the power quality standards of IEEE,the mathematical models of the normal voltage signal and three kinds of common short-time power quality disturbance signals are constructed,and the sparsity property of these signals is verified by the discrete Fourier transform and the discrete cosine transform.(2)The sparsity features of short-time power quality disturbance signals,e.g.voltage swell,voltage sag and voltage interruption,are developed,and we verify that the sparsity of signals in the frequency domain is even.(3)With the developed features,a reconstruction method referred to as double step-size sparsity adaptive matching pursuit(DS-SAMP)is proposed.Compared with the conventional sparsity adaptive matching pursuit(SAMP)algorithm,the analysis and simulation results show that the proposed method reduces the computational complexity and mean square error(MSE),improves the signal-to-noise ratio(SNR)and the probability of correct reconstruction.(4)By applying a limit quantization method,i.e.,single-bit compressed sensing theory,to short-time power quality disturbance signals sampling and compression,a reconstruction method referred to as residual selection-based sparsity adaptive binary iterative hard thresholding(RS-SABIHT)is proposed in this paper.Compared with the traditional single-bit compressed sensing,the simulation results show that the proposed method reduces the mean square error(MSE),improves the signal-to-noise ratio(SNR),energy recovery percentage(ERP)and the probability of correct reconstruction.With the same storage overhead,compared with the multi-bit quantization compressed sensing method,the proposed method reduces the mean square error(MSE),improves the signal-to-noise ratio(SNR)and energy recovery percentage(ERP).