Abstract
The proposed work focuses on photovoltaic (PV) system monitoring and the use of maximum power point tracking (MPPT) techniques for optimal power generation. It emphasizes the widespread application of artificial intelligence (AI) in MPPT methods in solar power systems, which can significantly improve performance and efficiency. However, AI-based MPPT techniques may be more computationally intensive and costly. Hybrid MPPT methods combine traditional and AI techniques to balance performance and complexity, addressing these issues. Fuzzy logic control (FLC) emerges as a viable technique, though synchronization is required for optimal performance under variable irradiation and weather conditions. This paper uses MATLAB/Simulink to compare the four main MPPT techniques: fuzzy-based variable step size P&O, FLC, and hybrid P&O-FLC algorithms. Further, a symmetric fuzzy controller, the perturb-and-observe (P&O) method, and a mixed method that combines both fuzzy and P&O-based controllers were compared. At the same time, many other FLC-based MPPT strategies were also examined. All tests were performed in a solar PV system with a DC-DC boost converter, considering varying atmospheric conditions. Overall, the study's aim is to provide insights into the relative effectiveness of various MPPT techniques, with a particular emphasis on the use of FLC and its hybrids in a variety of environmental scenarios.