Enhancing photovoltaic performance using artificial intelligence methods based on Fuzzy Logic

Abstract

Maximum power point tracking (MPPT) methods for photovoltaic (PV) systems have evolved from traditional techniques such as incremental conductivity (IC) to more advanced artificial intelligence-based methods such as fuzzy logic controllers (FLC). This study systematically compares the efficiency of two MPPT methods, IC and FLC, under different temperature and solar irradiation conditions.The FLC dynamically adjusts the DC-DC converter’s duty cycle to ensure accurate MPP tracking, while the IC uses traditional techniques to adjust the system operating point. Analysis in MATLAB/Simulink shows that FLCs consistently outperform ICs in energy efficiency, achieving efficiencies as high as $\mathbf{9 8 \%}$ under certain illumination conditions.The results highlight the importance of fuzzy logic-based methods for optimizing photovoltaic system performance, especially in the context of climate fluctuations. This approach provides a fast, oscillation-free response and improves the overall efficiency of MPP tracking in dynamic photovoltaic environments.