Enhanced Photovoltaic System Performance Using ABC-HC and Super Twisting Sliding Mode Control Under Partial Shading Conditions

Abstract

This research presents a hybrid optimization and control strategy to enhance the PV technologies by combining Artificial Bee Colonies with the Hill Climbing algorithm (ABCHC) for accurate tracking of the Global Maximum Power Point (GMPP) and maintaining robust system control by integrating Super Twisting-Sliding Mode Control (ST-SMC). The developed approach effectively addresses challenges related to dynamic environmental conditions and partial shading scenarios that affect the efficiency of PV systems. The ABC-HC algorithm correctly identifies GMPP due to its high capacity in determining the optimal reference voltage, while the ST-SMC technique ensures high stability, minimum chattering, and good adaptation with solar irradiance fluctuations. The hybrid strategy's effectiveness was validated in this study through MATLAB simulations, demonstrating its performance in GMPP tracking under moderate shading conditions (20-40%) with irradiance changes during the day. These results show that this idea is a good solution for maximizing energy output with high efficiency and resilience in practical PV technologies applications.