Adaptive Dictionary Learning Data Compression Algorithm for Distributed Photovoltaics

In order to promote the development of a low-carbon economy, many solar energy industries have emerged. The distributed photovoltaic power generation system can directly convert solar energy into electricity, which is a new type of clean and environmentally friendly system. The information monitoring network in the system provides photovoltaic power generation system data to help the power grid achieve collaborative optimization. In order to reduce the energy consumption in the data transmission process, an adaptive dictionary learning data compression algorithm based on compressed sensing theory is proposed. With the application of compressed sensing theory, it combines the sampling and compression process of signals, breaking the limitations of traditional Nyquis's theorem and greatly reducing the time of data processing. This paper combines adaptive dictionary learning with compressed sensing theory, first using input signal data for sparse coding, training adaptive dictionary to achieve rapid data compression, and finally using compressed sensing theory to reconstruct data.