Next-Generation MPPT: neural network-driven optimization for superior solar performance

This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV) systems and raised broadband oscillations. To address these issues, we propose an intelligent MPPT strategy utilizing NNC. The NNC inputs are the error and derived error (E&ΔE) between the reference and actual inputs, with the duty cycle (D) as the target output. The proposed algorithm demonstrates an efficiency of approximately 0.75% compared to 0.63% for the P&O method. Simulation and experimental validation results confirm the NNC strategy's effectiveness, robustness, and efficiency, providing a thorough system analysis.