Multi-Stage Cooperative Optimization Control for Photovoltaic MPPT: A High-Efficiency Gray Wolf Optimizer–Incremental Conductance Hybrid Strategy

With the continuous growth of global energy demand and the increasing severity of environmental issues, photovoltaic (PV) power generation, as a clean and renewable energy source, has attracted widespread attention. However, the performance of PV systems is easily affected by factors such as irradiance and temperature in complex environments, leading to significant fluctuations in output power and making it difficult to achieve stable and efficient energy conversion. To address this issue, this paper proposes an enhanced maximum power point tracking (MPPT) algorithm based on the combination of improved gray wolf optimizer (GWO) and incremental conductance (INC) methods, aiming to improve the adaptability and stability of PV systems in complex environments. By introducing innovative measures such as a candidate point dynamic focusing mechanism, position updates with perturbation factors, a five-level dynamic step-size strategy, direction consistency detection, and momentum suppression, the algorithm improves the search efficiency of the GWO and its adaptability to environmental mutations, avoids the drawbacks of fixed step sizes, and reduces overshoot and oscillations. Simulation verification was carried out on a simulation platform. The simulation results show that under various operating conditions, the algorithm achieves a good adaptive balance between global exploration and local exploitation, with tracking efficiency consistently above 99%, significantly improving the accuracy and efficiency of maximum power point tracking.