Enhancing Photovoltaic System Efficiency Through Fuzzy Logic-Based Maximum Power Point Tracking
This study utilizes a fuzzy logic controller (FLC), a type of computational intelligence tool, for determining a photovoltaic (PV) system's maximum power point (MPP). The solar PV system comprises of a PV module, an MPP controller, all of which are designed to monitor and optimize the highest point of energy generation in a photovoltaic system and a DC-DC boost converter. FLC is the core methodology employed in this study for maximum power point tracking (MPPT) within the converter-supplied PV system. The MPPT process involves the analysis of system parameters, including current error and the rate of change of errors, which serve as the input to the fuzzy logic system. The FLC continuously processes these inputs to adjust the duty cycle of the DC-DC boost converter, ensuring that the PV system operates at its highest energy generation potential. The 1Soltech 1STP-215-P PV model is utilized for this study, and it is representative of the PV model used in simulation studies. Photovoltaic systems are inherently nonlinear and respond to various external factors, making them less efficient. Therefore, the proposed intelligent control method, based on artificial intelligence principles, offers an easily implementable solution and provides valuable feedback. It exhibits robust performance even when solar irradiance levels fluctuate and excels at tracking MPPs, among other advantages. To simulate and evaluate the maximum power point tracking in a solar system, a PV system incorporating a fuzzy logic controller is designed and simulated using MATLAB/Simulink. The simulation results confirm the effectiveness of the proposed fuzzy logic controller in maximizing electricity generation from the PV panels while maintaining their operating voltage at an efficient level.
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