Evaluation of the performance of a FONN-based MPPT control for a photovoltaic watering system

This study aims to increase the effectiveness of photovoltaic pumping systems. A practical installation and cost-effective design are suggested. This paper examines the nonlinear behaviour of photovoltaic generators from a distinct perspective; where it repeatedly transitions between a constant current and a constant voltage source and shows how this affects the behaviour of the induction motors. A Fractional-order Neural Network (FONN) is suggested to forecast the harvested solar-energy. The results showed that FONN improved forecasting accuracy by effectively capturing the nonlinear behaviour of PV panels. A Fractional Order MPPT (FO-MPPT) control augmented with Gray Wolf, Anti-lion, and Whale metaheuristic optimizers is proposed and shows capacity to maximize the harvest power for the PV-driven Induction Motor-Pump. The proposed FO-MPPT is compared to conventional techniques using several performance metrics. According to the comparison study, the optimized FO-MPPT enhances the standard MPPT and shows superiority in managing the nonlinear and unpredictable dynamical loads.