A Comprehensive and Comparative Investigation of Maximum Power Point Tracking Algorithms for Photovoltaic Systems

This paper presents a detailed comparative analysis of conventional and AI-driven Maximum Power Point Tracking (MPPT) techniques utilized in photovoltaic (PV) systems. The study evaluates Fuzzy Logic Control (FLC) methods, the incremental conductance (INC) method, Perturb and Observe (P&O) methods with both fixed and variable step sizes, and the Simplified Model-Based State Estimation (SMSE-based) method. All simulations were rigorously conducted in the MATLAB/SIMULINK for ensuring consistent application of each technique, enabling a comprehensive comparison of their performance across various solar irradiance conditions. Results show that despite initial high oscillations, the SMSE-based method efficiently reaches the Maximum Power Point (MPP) with efficiency values of up to 99.9%. Fuzzy Logic Control Set-2 demonstrates exceptional performance with minimal oscillations and high efficiency, making it a robust choice for dynamic conditions. The Incremental Conductance method and Fuzzy Logic Control Set-1 exhibit moderate oscillations and longer times to reach the MPP, with efficiencies up to 99.7% and 99.6%, respectively. The P&O Variable Step method outperforms the Fixed Step method in both convergence speed and minimizing oscillations. This paper contributes to the evolution of MPPT techniques by highlighting the trade-offs between convergence speed, efficiency, and stability, offering valuable insights for optimizing PV system performance.