Data Denoising and Compression for Smart Grid Communication

A technique based on wavelet packet decomposition (WPD) is proposed for the analysis, denoising, and compression of power system data in the smart grid (SG) communication. WPD is an expansion of wavelet decomposition (WD) tree algorithm to a full binary tree. The main advantage of WPD is better signal representation by finding the best tree from a number of bases of the WPD. Thus, the wavelet packet analysis provides satisfactory preservation of feature integrity and removal of redundancy to achieve better noise reduction and higher compression with controlled degradation in data fidelity. A cost function is used in this work for efficient searching of full binary tree of WPD to obtain an accurate representation of a given signal. The data analysis, compression, and denoising are performed through selection of a suitable wavelet function, decomposition the signal up to the optimum level, determination of the best tree representation, calculate threshold at different levels, application of the threshold to the coefficients, and reconstruction of the signal. The proposed method is evaluated using a set of frequency disturbance recorder (FDR), phasor measurement unit (PMU), and load voltage data. Comparative results are presented with the wavelet decomposition (WD) and the built-in Matlab function 'wpdencmp.'