A 2-D information reordering data compression method for power system

A novel data compression method was developed for periodical data in power system. Based on the unbalanced nature of information in cycles and between cycles, it can eliminates the coupling of information through automatic adjustment of sampling frequency and achieve large compression ratio. In order to reduce the redundancy more efficiently, a 2 variable linear regression system is developed to predict the frequency of the power system and to realize synchronous sampling. After that, the data is compressed based on lifting wavelet decomposition method. The real-life periodical data are used to test this method. The result indicates that the proposed method can achieve better performance comparing to the method based on asynchronous sampling. For the same compression ratio, the synchronous sampling can achieve much higher signal-to-noise ratio than asynchronous sampling method.