Optimal Incremental Conductance-Based MPPT Control Methodology for a 100 KW Grid-Connected PV System Employing the RUNge Kutta Optimizer

Abstract

Solar energy is a promising and sustainable green energy source, showing significant advancements in photovoltaic (PV) system deployment. To maximize PV efficiency, robust maximum power point tracking (MPPT) methods are essential, as the maximum power point (MPP) shifts with changing irradiance and temperature. This paper proposes a novel MPPT control strategy for a 100 kW grid-connected PV system, based on the incremental conductance (IC) method and enhanced by a cascaded Fractional Order Proportional–Integral (FOPI) and conventional Proportional–Integral (PI) controller. The controller parameters are optimally tuned using the recently introduced RUNge Kutta optimizer (RUN). MATLAB/Simulink simulations have been conducted on the 100 kW benchmark PV model integrated into a medium-voltage grid, with the objective of minimizing the integral square error (ISE) to improve efficiency. The performance of the proposed IC-MPPT-(FOPI-PI) controller has been benchmarked against standalone PI and FOPI controllers, and the RUN optimizer is here compared with recent metaheuristic algorithms, including the Gorilla Troops Optimizer (GTO) and the African Vultures Optimizer (AVO). The evaluation covers five different environmental scenarios, including step, ramp, and realistic irradiance and temperature profiles. The RUN-optimized controller achieved exceptional performance with 99.984% tracking efficiency, sub-millisecond rise time (0.0012 s), rapid settling (0.015 s), and minimal error (ISE: 16.781), demonstrating outstanding accuracy, speed, and robustness.