Distributed Predictive Control of Grid-Connected Solar PV Generation Based on Data-Driven Subspace Approach

The scale of a distributed grid-connected solar photovoltaic (PV) generation system keeps growing, which is naturally composed of many subsystems interacting with each other. In this paper, a novel distributed predictive control based on data-driven subspace approach is proposed to design the predictive controller of the grid-connected inverter in the distributed solar PV generation system. The control performance of the whole system is improved by minimizing the interaction between solar PV generation subsystems and the grid, as well as the interactions within subsystems. Each solar PV generation subsystem has its own inverter which converts the induced energy into the power in utility grid and exchanges the input and output information with each other through network. The application of data-driven predictive method in the grid-connected solar PV generation system is analyzed in detail in this paper. The simulation model of the distributed grid-connected solar PV generation system is established in Matlab/Simulink, which validates the good performance of the approach. The results demonstrate the effectiveness of the proposed control method.