中文摘要：

针对电力系统大量周期性实时数据,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,基于三次样条插值方法进行重采样以实现整周期采样,克服电网频率波动的影响,消除循环内与循环间信息的耦合,更有效去除循环间数据的冗余性,实现大压缩比。分析了该重采样方法的误差,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所提出的算法进行验证,试验结果表明,在相同的压缩比下,重采样之后进行压缩的数据的信噪比优于直接压缩数据的信噪比。

英文摘要：

A novel data compression method is developed for periodical data in power systems. Considering the unbalanced nature of information in cycles and between cycles, coupling of information caused by power system frequency fluctuation and non-integer-period sampling are eliminated based on the cubic spline interpolation re-sampling method to achieve large compression ratio. The error caused by re-ampling is analyzed. The data are compressed based on the lifting wavelet decomposition method. The real-life periodical data are used to test the proposed method＇s performance. The results indicate that much higher signal-to-noise ratio can be achieved than a compressing data method without the re-sampling process for the same compression ratio.