Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition

• Main Authors: Yew W.H., Fat Chau C., Mahmood Zuhdi A.W., Syakirah Wan Abdullah W., Yew W.K., Amin N.
• Other Authors: 58765606400
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: deep reinforcement learning; energy; maximum power point tracking (MPPT); off-grid PV; partial shading conditions (PSC); particle swarm optimization (PSO); perturb and observe (P&O); Deep learning; Global optimization; Learning algorithms; MATLAB; Maximum power point trackers; Particle swarm optimization (PSO); Renewable energy resources; Condition; Deep reinforcement learning; Energy; Maximum Power Point Tracking; Off-grid PV; Off-grids; Partial shading; Partial shading condition; Particle swarm; Particle swarm optimization; Perturb and observe; Reinforcement learnings; Swarm optimization; Reinforcement learning
• Institution: Universiti Tenaga Nasional

For renewable energy systems to operate as efficiently and as effectively as possible, maximum power point tracking (MPPT) controllers are essential. They make it possible to precisely and dynamically track the peak output of solar panels or wind turbines, ensuring that the system will be stable and...