Power prediction-based photovoltaic hybrid MPPT control strategy

Addressing the challenges associated with the single maximum power point tracking (MPPT) control strategy for photovoltaics, which often struggles to rapidly and accurately track the global maximum power point (GMPP) of the photovoltaic array in complex external environments, leading to significant power oscillations, we propose a photovoltaic compound MPPT control method grounded in power prediction (PF-IAFSA-IP&O). This innovative approach utilizes an enhanced photovoltaic current sorting kinematic projectile model to establish a photovoltaic power prediction model. The model is then solved using an improved adaptive artificial fish swarm algorithm (IAFSA), enabling the photovoltaic array to swiftly operate at the maximum power point. Furthermore, to mitigate prediction model errors, the adaptive perturb and observe (IP&O) algorithm is employed for local search, ensuring more precise optimization. Simulation results demonstrate that our proposed MPPT control strategy offers significant improvements compared to traditional methods, effectively reducing power oscillations during the maximum power tracking process.