A hybrid global wolf pack algorithm-based incremental conductance method under partial shading conditions

Abstract

Solar photovoltaic (PV) power generation plays a crucial role in addressing energy shortages, promoting energy conservation, and reducing emissions. The efficiency and reliability of PV systems are enhanced by maximum power point tracking (MPPT) technology, which also helps to lower application costs. However, traditional MPPT methods often get trapped in local optima under partial shading, while intelligent methods fail to eliminate steady-state oscillations, causing unnecessary power losses. Therefore, a hybrid global wolf pack algorithm-based incremental conductance method (GWPA-FINC) is proposed to reduce oscillation along with improving the identification of the global maximum power point (GMPP) region under partial shading conditions. GWPA-FINC’s performance is evaluated in seven scenarios: static and dynamic partial shading (scenario 1 to scenario 4), scenarios of varying temperature (scenario 5 to scenario 7). A comparative analysis is conducted with Particle swarm optimization (PSO), gray wolf optimization (GWO), sand cat swarm optimization (SCSO), wolf pack algorithm (WPA) and global wolf pack algorithm (GWPA), focusing on steady-state oscillation. Across all experimental scenarios, the power oscillation reduction up to 92.37% and voltage oscillation reduction up to 95.86% are achieved, with a tracking efficiency of 99.99%. These results indicate that the proposed GWPA-FINC effectively addresses the GMPP optimization challenge, significantly enhancing both steady-state performance and system efficiency under shading conditions.