Adaptive MPPT Algorithm Based on Grey Wolf Optimization for Photovoltaic Systems in Dynamic Weather Conditions

Photovoltaic (PV) systems are essential for renewable energy production, but their efficiency is challenged by dynamic weather conditions such as varying irradiance and temperature, leading to fluctuations in power output. Traditional Maximum Power Point Tracking (MPPT) methods, like Perturb and Observe (P&O), often suffer from oscillations and slow response times under rapid changes. This paper proposes a novel self-adaptive MPPT algorithm integrating Grey Wolf Optimization (GWO) to enhance tracking efficiency and reduce power losses. The GWO-driven variable step mechanism simulates wolf hunting behavior to optimize the search for the Maximum Power Point (MPP), adapting dynamically to environmental variations. Experimental evaluations using real-world solar data from Kaggle demonstrate that the proposed GWO-MPPT achieves an average efficiency of $\mathbf{9 6 . 3 7 \%}$, surpassing P&O’s $\mathbf{9 6 . 0 9 \%}$ and significantly outperforming Whale Optimization Algorithm (WOA) in cumulative energy output ($226,418.2 \mathrm{~Wh}$ vs. P&O’s $230,627.3 \mathrm{~Wh})$. Key performance metrics, including response time (3 iterations) and power loss (1.87 W), highlight the algorithm’s superiority. Comparative analyses with benchmarks confirm reduced oscillations and faster convergence, making it suitable for practical PV deployments. Future enhancements could include hardware integration for real-time validation, further optimization with noise filtering, and hybrid integration with other algorithms like PSO for enhanced robustness.