AIC-based Distributed Photovoltaic Data Compression Sensing Method

In view of the high-speed data transmission requirements of the new generation of power line carrier communication in distributed photovoltaic information access, data compression sensing is required to improve. In this paper, we study an AIC-based distributed photovoltaic data compression sensing method. Based on the adaptive characteristics of K-singular value decomposition (K-SVD) algorithm, the data sparsity is dynamically estimated to realize the adaptive dynamic adjustment of data acquisition. AIC algorithm is used to reconstruct data, which greatly improves the accuracy of data reconstruction. Information aggregation based on compressed sensing is constructed to achieve high efficiency data aggregation under the condition of low sampling rate. Simulation results show the superior performance of the proposed algorithm in the data transmission volume and energy consumption.