Reinforcement Learning Techniques for MPPT Control of PV System Under Climatic Changes

Abstract

Photovoltaic (PV) systems have become a potential solution to global problems like pollution and climate changes resulting from the excessive use of fossil fuels. This kind of system can respond to the constant increase in the electric energy demand and the need for energy supply in rural or hard-to-reach areas. However, as the energy efficiency of PV systems is low, there exists a necessity to maximize the output power so that it reaches the maximum power point (MPP). Different Maximum Power Point Tracking (MPPT) techniques can be used to increase the efficiency of PV systems. Nevertheless, climatic variations make their task difficult to achieve. This work proposes the use of reinforcement learning (RL) techniques for solving the MPPT problem of a PV system under different conditions of temperature and solar irradiance. RL techniques do not require information of a model that describes the behavior of the system with its environment. They only make use of the information of the possible states to visit and actions to take and updates a utility function according to how good the last action taken was. To validate the effectiveness of the proposed algorithms, several experiments were performed in a simulated environment. The obtained results show good performances with stable behaviors, proving to be practical for the control of photovoltaic systems.