A Wavelet Dictionary-Based Compressive Sensing Method for Reconstruction of Wind Speed Data
Wind speed is usually non-stationary and not naturally sparse.The commonly used dictionary for the compressed sensing(CS)method is not effective for the reconstruction of the wind speed signals.In this paper,the wavelet dictionary is introduced to improve the sparsity of wind speed signals and effectively enhance the ac-curacy of the reconstructed signal.The effectiveness of this method is verified using both wind speed simulation data and monitored wind speed data of Canton Tower.The effects of data missing scenarios,regularization pa-rameters,wavelet dictionary layers,and wavelet types on the reconstruction performance of the CS method are explored in detail.The results show that the wavelet dictionary-based CS method has high accuracy in recon-structing the missing wind speed signals.
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