Performance Evaluation of Intelligent MPPT Algorithms for Enhanced Power Extraction in Standalone Photovoltaic Systems

The objective of the present study is to compare the output of classical and intelligent maximum powerpoint tracking (MPPT) algorithms to improve power extraction in a standalone photovoltaic (PV) system using apositive output super-lift Luo converter.Based on the three-level voltage source inverter, positive output super-lift Luoconverter, PV array, and battery storage, A MATLAB/Simulink model was created.INC, Genetic Algorithm (GA),Gray Wolf Optimization (GWO), and fuzzy logic-based MPPT controllers were applied and tested under the sameconditions of irradiance and temperature.These performance indicators are the PV voltage, PV current, and outputpower.The comparison outcomes prove that smart MPPT strategies are better than the traditional INC tool in theextraction of power and correct tracking.The fuzzy-logic-based MPPT is the best of the studied approaches because ithas better regulation of its duty-cycle, fewer oscillations around the maximum power point, and better steady-stateoperation than GA and GWO algorithms.The originality of this publication is the simultaneous performance analysisof classical and intelligent MPPT algorithms with a positive output super-lift Luo converter applied to a standalonePV system, and the appropriateness of fuzzy logic masked control towards improved photovoltaic energy collection.