P. Poorna Priya, Albert Alexander Stonier
Ongoing improvements in MPPT methods are essential for boosting the energy production and cost-effectiveness of solar photovoltaic (PV) systems. This review explores foundational PV modelling approaches, including single-diode, double-diode, and three-diode models that accurately reflect the solar cells’ nonlinear properties in different environmental settings. Photovoltaic characteristics are analyzed through P–V and I–V curves across different scenarios, including standard irradiance, partial shading, and dusty environments. MPPT techniques are categorized into single- and multiple-MPPT methods, the latter being essential for distributed systems with multiple local maxima. The study classifies MPPT algorithms into four categories: conventional (P&O, HC, IncCond, etc.), intelligent (FL, ANN, GNT, etc.), optimization-based (PSO, GA, CS, etc.), and hybrid methods (AN-FIS, F-PSO, GWO-P&O, etc.). Each technique is evaluated for accuracy, convergence speed, computational demand, sensors, tracking performance, cost, and environmental adaptability. While conventional methods are simple and cost-effective, they suffer under dynamic conditions. Intelligent and optimization-based methods offer enhanced performance but require significant computational resources and precise data. Hybrid approaches balance speed, robustness, and implementation complexity, making them suitable for real-time systems. The review further explores machine learning-based MPPT methods (SVMs, RL, and CNNs), hardware constraints, and cost considerations. Special focus is placed on MPPT strategies for emerging technologies like PSCs, which demand tailored control due to unique electrical characteristics. The impact of MPPT algorithms on power quality, especially THD, is also discussed alongside the role of multilevel inverters. Furthermore, the review outlines recent advancements in MPPT research and discusses the gaps in this field. Finally, future directions include the integration of IoT, edge computing, and AI for predictive, adaptive, and high-efficiency MPPT.