Experimental Validation of a Global MPPT Based on Bald Eagle Search Technique

The issue of partial shading condition (PSC) poses a significant obstacle for Photovoltaic (PV) systems, resulting in notable declines in energy production. This condition introduces multiple local maximum power points (MPP) in the PV curve, which can hinder both traditional and modern MPPT controllers from operating the PV system at its optimal peak values. Therefore, the output power of the P-V system is further diminished. This study proposes a solution known as the adaptive global MPP (GMPP) method, which utilizes the Bald Eagle Searching (BES) technique to tackle this challenge. The implementation and verification of the GMPP tracking method were performed using MATLAB, demonstrating its effectiveness. To validate its performance, experiments were conducted at the Sultan Qaboos University Hybrid Station Lab using a real test platform. These experiments encompassed various conditions, including transitioning between partial shading and no shading scenarios, as well as changes in temperature and irradiance. The experimental results confirm that the proposed adaptive GMPP BES-based tracking method exhibits exceptional performance across diverse operating conditions. The method consistently achieves outstanding tracking accuracy, thereby ensuring optimal power generation from the PV system.