Publication

Abstract : An essential part of solar photovoltaic (PV) systems is maximum power point tracking (MPPT), which allows for the best possible energy extraction in a variety of environmental circumstances. In dynamic settings, traditional MPPT methods like perturb and observe (P&O) and incremental conductance (INC) frequently fail, especially when there is partial shade or quickly varying irradiance. One possible approach to overcoming these obstacles is reinforcement learning (RL), a subfield of artificial intelligence (AI) that focuses on learning via interactions with the environment. An overview of RL-based methods for MPPT control in solar PV systems is given in this paper, with special attention paid to important algorithms such as proximal policy optimization (PPO), Deep Q-Networks (DQN), Q-Learning, and deep deterministic policy gradient (DDPG). The survey looks at how RL frameworks establish reward functions, describe the MPPT problem as a markov decision process (MDP), and adjust to contexts with continuous or discrete control. The benefits of RL in terms of efficiency, global optimization, and real-time learning are revealed through comparison with traditional and other techniques. An overview of RL-based MPPT techniques is detailed in this study, along with a discussion of their basic ideas, algorithmic modifications, performance benefits, and possible drawbacks. The report also emphasizes current research trends, highlighting RL’s contribution to the development of intelligent control strategies for PV power management systems of the future.