REVIEW OF REINFORCEMENT LEARNING TECHNIQUES FOR MPPT PHOTOVOLTAIC APPLICATION
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Abstract
The integration of renewable energy systems, particularly photovoltaic (PV) technology, has become increasingly essential in addressing global energy demands and environmental concerns. However, the nonlinear and time-varying characteristics of solar irradiation and temperature significantly affect the efficiency of PV systems. Maximum Power Point Tracking (MPPT) techniques are employed to ensure that PV modules operate at their optimal power point under varying conditions. Traditional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) offer simplicity but often suffer from oscillations, slow convergence, and reduced performance under dynamic conditions.
This research investigates the application of reinforcement learning (RL) algorithms for MPPT control in PV systems, aiming to enhance tracking speed, stability, and adaptability. By formulating the MPPT problem as a sequential decision-making process, RL agents learn optimal control policies through continuous interaction with the PV environment without requiring explicit system modeling.
Various RL approaches—such as Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods—are analyzed and compared with conventional techniques through simulation studies. The results demonstrate that RL-based MPPT controllers can effectively handle rapidly changing environmental conditions, minimize steady-state oscillations, and achieve superior energy harvesting efficiency. This study highlights the potential of reinforcement learning as a robust and intelligent solution for real-time PV power optimization
This research investigates the application of reinforcement learning (RL) algorithms for MPPT control in PV systems, aiming to enhance tracking speed, stability, and adaptability. By formulating the MPPT problem as a sequential decision-making process, RL agents learn optimal control policies through continuous interaction with the PV environment without requiring explicit system modeling.
Various RL approaches—such as Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods—are analyzed and compared with conventional techniques through simulation studies. The results demonstrate that RL-based MPPT controllers can effectively handle rapidly changing environmental conditions, minimize steady-state oscillations, and achieve superior energy harvesting efficiency. This study highlights the potential of reinforcement learning as a robust and intelligent solution for real-time PV power optimization
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