AI-Powered Tuning of Nonlinear Fractional PI Controllers for High-Efficiency MPPT in PV Systems

Photovoltaic (PV) systems are among the most widely used sources of renewable energy, offering a promising alternative to conventional energy. However, their efficiency is significantly affected by weather variations, leading to fluctuations in output voltage and power generation. To maintain a stable output voltage, DC converters require robust control strategies that take into account their non-linear behavior. This work presents a maximum power point tracking (MPPT) control strategy based on the incremental conductance method, enhanced by a fractional-order proportional-integral (FOPI) controller. Additionally, a novel non-linear controller topology (FONPI) is introduced by integrating a non-linear gain to the conventional FOPI controller, the modification helps cope with the non-linear nature of PV systems and improve the dynamic response to abrupt environmental changes. The controller parameters are optimized using two metaheuristic algorithms: Grey Wolf Optimizer (GWO) and Ant Lion Optimizer (ALO). System performance is evaluated through metrics including overshoot, settling time, and efficiency, demonstrating the effectiveness of the proposed approach.