Maximum power point tracking (MPPT) is essential for improving the energy harvesting performance of grid-connected photovoltaic systems under varying operating conditions. However, the growing diversity of MPPT algorithms has made the selection of suitable control strategies increasingly challenging for researchers. This review presents a comprehensive analysis of MPPT techniques for grid-connected photovoltaic systems, with particular emphasis on dynamic environmental variations, partial shading conditions, and grid-interfacing requirements. The study systematically classifies and evaluates conventional methods, intelligent control approaches, and bio-inspired optimization techniques. In contrast to earlier review articles that mainly emphasize traditional methods such as Perturb and Observe and Incremental Conductance, this work focuses on two distinctive aspects: the comparative literature compilation of modern artificial intelligence and metaheuristic-based MPPT algorithms; and the inclusion of power quality considerations in MPPT performance evaluation. Quantitative assessment metrics derived from various experimental conditions are aggregated to provide a broader comparison of control strategies. In addition, the impact of MPPT methods on power quality parameters, particularly total harmonic distortion and power factor, is examined. The review further summarizes recent advances in metaheuristic optimization for challenging operating scenarios and identifies key research gaps. Finally, practical guidelines are provided for selecting and developing MPPT strategies for residential, commercial, and utility-scale photovoltaic applications, with particular attention to sensorless and grid-aware control solutions for future power networks.
Ullah et al. (Wed,) studied this question.
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