Photovoltaic MPPT Algorithm Based on Improved Gray Wolf Optimizer for Partial Shading Conditions

When photovoltaic arrays operate under partial shading conditions (PSC), the output power-voltage (P-V) curves may exhibit nonlinear and multi-extreme characteristics, reducing the power conversion efficiency. More robust intelligent optimization algorithms for maximum power point tracking (MPPT) are necessitated. However, existing MPPT algorithms suffer from slow convergence speeds, a tendency to get trapped in local extrema, and low tracking accuracy. This paper proposed an MPPT control method based on an improved grey wolf optimizer (IGWO). The algorithm introduces a piecewise adjustable attenuation factor to balance the search range in the early and late stages of the algorithm. Additionally, to address the multiple extrema issue under PSC, the algorithm incorporates the Lévy flight strategy to enhance global search capability. Moreover, a random perturbation strategy is introduced to strengthen local exploration and improve the tracking accuracy. Extensive experimental results under various PSCs demonstrate that the proposed IGWO-based MPPT algorithm achieves a minimum tracking time of 13 ms and a maximum tracking accuracy of 99.93%. Compared to various benchmarking algorithms, the proposed method offers shorter tracking time and higher accuracy.