电力系统二维重组法数据压缩算法

A 2-D Information Reordering Data Compression Method for Power System

提出了一种新的数据压缩与解压缩算法,针对周期性数据循环内与循环间信息不均衡性,能够在电网频率波动的情况下,通过自动调整采样频率,消除周期性数据循环内与循环间信息耦合,更有效地去除循环间数据冗余性,实现电力系统周期性数据大比率数据压缩。采用了二元线性回归算法对电网频率进行实时预测,提出同步数字倍频法用来产生同步采样脉冲,进而实现等相位同步采样,利用基于提升格式的小波分解对数据等相位点序列分别进行分解与重构,实现数据压缩与解压缩。利用实际测取的电力生产过程中的周期性数据对算法进行验证,试验结果表明,在相同的压缩比下,基于等相位采样的重构效果优于基于等间隔采样的重构效果。

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.