A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition

Area of Science:

• Renewable Energy Systems
• Environmental Protection
• Artificial Intelligence in Energy

Background:

• Photovoltaic (PV) systems are crucial for reducing fossil fuel dependence and environmental pollution.
• Efficient operation of PV systems relies on Maximum Power Point Tracking (MPPT) algorithms under varying weather conditions.
• Deep Reinforcement Learning (DRL) offers advanced optimization control for complex systems.

Purpose of the Study:

• To propose and evaluate Deep Reinforcement Learning (DRL) algorithms for optimizing Maximum Power Point Tracking (MPPT) in PV systems.
• To address challenges in PV system efficiency, particularly under partial shading conditions (PSC).
• To compare DRL-based methods with traditional MPPT algorithms.

Main Methods:

• Implementation of Deep Q Network (DQN) for discrete action spaces and Deep Deterministic Policy Gradient (DDPG) for continuous action spaces.
• Simulation of proposed DRL algorithms in MATLAB/Simulink.
• Validation through comparison with the classical Perturb and Observe (P&O) MPPT method under various input conditions.

Main Results:

• DRL-based methods (DQN and DDPG) demonstrated outstanding performance in harvesting the Maximum Power Point (MPP) of PV systems.
• The proposed methods proved efficient, especially under partial shading conditions (PSC).
• Simulations confirmed the feasibility and effectiveness of DRL for PV system optimization.

Conclusions:

• DRL algorithms, specifically DQN and DDPG, are highly effective for MPPT in PV systems.
• These advanced methods show significant potential for future applications in renewable energy optimization.
• DRL provides a robust solution for efficient PV energy harvesting, outperforming traditional methods.