A comprehensive study of recent maximum power point tracking techniques for photovoltaic systems.

The percentage of renewable energy in the global mix of energy sources is rising annually, with solar photovoltaics (PVs) accounting for most capacity expansions due to their widespread availability, safety, and cleanliness. Because the amount of energy generated is limited by the poor efficiency of the photovoltaic cells and the characteristics of the connected load and weather fluctuation, maximum power point tracking (MPPT) strategies are crucial for maximizing the power delivered in PV production systems. These MPPT techniques face several issues and limitations, so this paper has focused more on modeling and developing the MPPT techniques in PV systems. The MPPT-based methodologies fall into three categories: artificial intelligence (AI), metaheuristic, and conventional. Five of these techniques have been proposed here to solve the MPPT problem. The perturb & observe (P&O) and incremental conductance (INC) methods have been used as conventional methods. In contrast, particle swarm optimization (PSO) has been used as a metaheuristic method. Finally, the artificial neural network (ANN) and fuzzy logic control (FLC) techniques have been used as AI methods. Each technique is analyzed critically in terms of tracking speed, algorithm complexity, and dynamic tracking in different environmental conditions. Furthermore, this comprehensive study of MPPT methods aims to be a guideline for selecting the best MPPT method for optimal operation under the environmental conditions of PV systems by employing multi-criteria decision-making (MCDM) based on AHP and CRITIC weighting methods, as well as the ranking method (VIKOR), to compare and rank the MPPT methods based on their effectiveness and economic feasibility. The results show AI techniques have a tracking efficiency of almost 99% when compared to other examined approaches, and they give quick and efficient tracking speed.