Performance of the Rapid Convergence Time for The Perturb and Observe MPPT Algorithm by Using Harris Hawks Optimization in Photovoltaic Systems

The efficiency of Maximum Power Point Tracking (MPPT) algorithms is crucial for optimizing the performance of photovoltaic (PV) systems. Traditional methods like the Perturb and Observe (P&O) algorithm are commonly used due to their simplicity, but they often suffer from issues such as slow convergence and oscillations around the maximum power point under changing environmental conditions. This study introduces an enhanced P&O algorithm by integrating it with Harris Hawks Optimization (HHO), a nature-inspired optimization technique known for its robust convergence characteristics. The proposed hybrid P&O-HHO algorithm aims to accelerate convergence time and improve the overall tracking performance of the PV system. To maximize power from PV modules at varying sun irradiance levels, Harris-Hawks Optimization (HHO) is offered as a performance improvement method for the conventional Perturb and Observe (P&O) approach in photovoltaic systems. The proposed model encompasses a boost converter for DC-DC power controlled by an MPPT algorithm, a PV panel, and a resistive load. The MPPT algorithm proposed is founded upon the execution of a hybrid technique that combines Harris-Hawks Optimization, a new method inspired by nature, and the conventional P&O approach. The suggested method has been tested through simulation testing utilizing the environment created by MATLAB Simulink. The findings of the simulation illustrate that‎‎ the HHO-P&O MPPT algorithm, as described, successfully ‎identified the global maximum power point more ‎efficiently.‎ Additionally, it exhibited a rapid convergence speed, superior outcomes in comparison to the standard Perturb and Observe method, and a swift dynamic reaction.