Maximizing energy yield from high-power photovoltaic (PV) systems is essential for grid stability and economic viability, particularly under rapidly fluctuating environmental conditions. This study presents a comprehensive dynamic comparison of three prominent Maximum Power Point Tracking (MPPT) strategies—Perturb and Observe (P&O). (Eltamaly AM, Farh HM, Othman MF, Solar Energy. 174:940–56, 2018). Fuzzy Logic Control (FLC). (Hassan et al. Renew Energy Focus. 48:100545, 2024), and Artificial Neural Network (ANN). (Çırak CR, Çalık H, Eng Sci Technol Int J. 43:101436, 2023)—in a 2 MW grid-connected PV system modeled in MATLAB/Simulink. The controllers are evaluated under both standard and rapidly changing irradiance conditions, with key performance metrics including tracking accuracy, convergence speed, stability, and overall energy yield. Results demonstrate the superiority of intelligent controllers under dynamic scenarios, with the ANN-based MPPT achieving the highest tracking efficiency (98.9%) and fastest settling time (1.5 s), outperforming conventional P&O and FLC methods as well as a state-of-the-art Adaptive P&O benchmark. (Arockiasamy B, Kuppusamy, Int J Precious Eng Res Appl. 8(1):1–26, 2023). The study confirms that data-driven and hybrid intelligent strategies significantly enhance PV system performance under partial shading and environmental fluctuations. These findings provide critical guidance for selecting optimal MPPT strategies for high-power, grid-interfaced PV installations, highlighting the potential of ANN-based controllers to improve reliability and efficiency in large-scale solar energy systems.
Bresnav et al. (Wed,) studied this question.