An Approach for Compressive Sampling and Reconstruction of Transient and Short-Time Power Quality Disturbance Signals

Traditional methods for acquisition and compression of power quality signals are faced with such troubles as high sampling rate,waste of sampling resources and high attainable cost for hardware.To solve these problems,based on compressive sensing theory a method for compressive sampling and reconstruction of transient and short-time power quality disturbance signals is proposed for the first time.In the proposed method one-dimensional power quality signals are mapped to two-dimensional signals,and according to the principle of image sparse representation original signals are reconstructed by sampled random projection values and the sampled data to be used in this way is reduced by more than 60% than those used in Nyquist sampling,thus the compressive sampling,the selection of sparse basis in data space and the signal reconstruction based on total variation conjugate gradient minimization method are implemented.Simulation analysis and experimental verification of the proposed method are performed by the most common single disturbances and measured signals of calibration source containing multiple disturbances,simulation and verification results show that when sampling rate of the proposed method is lower than 73% of that for Nyquist sampling,except transient impulse signals the S/N ratios of the reconstructed signals of single disturbances are greater than 35dB,the S/N ratios of the reconstructed signals of multiple disturbances are greater than 22dB,thus the requirement of power quality analysis can be met.