Power quality data compression based on sparse representation and compressed sensing

A power quality data compression method combining compressive sampling with adaptive matching pursuit reconstruction based on compressed sampling theorem is presented to solve the massive power quality data collection, compression and storage problems. First, the original power quality data was sampled and compressed simultaneously by random matrix projection method based on compressed sampling theorem. Then the proposed adaptive matching pursuit reconstruction algorithm was used to achieve accurate power quality data reconstruction. The proposed method breaks through the traditional framework of data compression by merging compression into sampling process and could reconstruct original power quality data from the small amount of sampling points from the compressed data. Simulation shows the proposed CS-based power quality data compression method can not only reduce hardware requirements, but also increase the efficiency of data compression.