Compression of Power Quality Disturbance Using Wavelet-Atomic Decomposition for Grid-Connected Wind Farms

The intermittency, volatility, and randomness of the power output of wind farms lead to serious power quality problems at the point of common coupling (PCC) of wind farms. Many waveform recording signals have high requirements for data transmission and storage, which is not conducive to wind power disturbance data analysis. Therefore, a power quality disturbance data compression method based on integrated wavelet and atomic decomposition is proposed to improve the compression ratio and computational complexity. According to the characteristics of power quality disturbance signal, a small-scale coherent atomic dictionary was established to characterize the disturbance components. Based on the small-scale atomic dictionary, the main components of the signal are represented in parametric atomics by atomic decomposition with the matching pursuit algorithm to achieve data compression. Then the irregular residuals of the signal are further decomposed by wavelet method to improve the effectiveness. Simulation results indicates that the proposed method inherits the high compression ratio of atomic decomposition, and further reduces the computational complexity of residual signal decomposition by wavelet transform, obtaining high compression ratios along with high signal-to-noise ratios.