Assessing the Effectiveness of ANN-Based MPPT in Enhancing Energy Efficiency in Floating Photovoltaic Pumping Systems

This abstract presents a thorough investigation into the performance of perturb and observe (P&O) and artificial neural network (ANN)-based maximum power point tracking (MPPT) techniques in floating photovoltaic (FPV) pumping systems. Utilizing water bodies strategically conservates land and enhances energy efficiency through aquatic cooling effects. Simulation results reveal a clear superiority of the ANN-based approach over P&O in tracking the maximum power point, resulting in significantly higher efficiency and power output. The study emphasizes the potential of ANN-based MPPT techniques for optimizing FPV pumping systems, providing valuable insights for future research. It underscores the multifaceted advantages of symbiotically integrating solar technology with water bodies, reaffirming its role as a sustainable and efficient energy solution with broader implications for renewable energy applications.