基于压缩感知的智能电能表信号压缩

需求响应与功率分解等复杂任务对于高采样率功率信号的精确获取提出了更高的要求。针对这一问题,设计一种基于压缩感知(Compressed Sensing,CS)的智能电能表功率信号压缩方法,以支持智能电能表的窄带宽、低能耗条件下的信号传输。分析了各类负载的特性,提出了对于智能电能表功率信号的经验模型,作为信号压缩的基础。通过实验验证了经验模型的有效性,并得到了适用于压缩感知方法的最优表示矩阵与投影矩阵生成方案,说明了压缩感知方法相比基于小波变换的压缩方法在信号重建效果上的优势。

Signal Compression for Smart Meters Based on Compressed Sensing

Power signal with high sample rate is more required for complex tasks including demand response and power disaggregation. For this issue, a signal compression method based on compressed sensing (CS) is designed, to support signal transformation under low energy consumption and narrow bandwidth condition in smart meters. On the basis of analysis on characteristics of different types of loads, an empirical model for power signal of smart meters is proposed. Experimental results demonstrate the validity of the empirical model, the best method of generating the representation matrix and projection matrix, and the superiority of compressed sensing based method compared to wavelet transform.