Improving the Performance of an Incremental Conductance MPPT Algorithm Using Harris-Hawks Optimization in Photovoltaic Systems

Renewable energy's rising demand has driven significant research into optimizing photovoltaic (PV) systems, with Maximum Power Point Tracking (MPPT) algorithms being crucial for maximizing energy extraction under varying conditions. The widely-used Incremental Conductance (INC) algorithm performs well in steady-state conditions but faces challenges like slow convergence and inefficiency during rapid irradiance changes. To address these limitations, this paper introduces a hybrid MPPT algorithm combining the INC method with Harris Hawks Optimization (HHO), a bio-inspired meta-heuristic that mimics the hunting behavior of Harris hawks. The HHO algorithm excels in balancing exploration and exploitation, making it effective in optimizing dynamic systems and avoiding local minima. By integrating HHO with INC, the hybrid approach dynamically adjusts key parameters, such as step size and convergence criteria, to track the global Maximum Power Point (MPP) more efficiently, especially rapidly changing conditions. The adaptive capabilities of HHO improve the accuracy and speed of the INC algorithm, reducing power losses and enhancing system robustness. Extensive simulations demonstrate that the hybrid HHO-INC algorithm significantly outperforms the traditional INC approach. It achieves faster convergence to the MPP, reduces the time spent in suboptimal regions, and shows higher tracking efficiency under fluctuating conditions. Moreover, the hybrid algorithm adapts quickly to environmental changes without requiring manual intervention or parameter tuning. This research highlights the potential of hybrid MPPT algorithms to enhance the performance and reliability of renewable energy systems, offering a scalable solution for improving energy efficiency in complex, dynamic PV environments.