MPPT Strategies for PV Systems Under Partial Shading: A Survey with Emphasis on the Artificial Bee Colony Algorithm

Ensuring the reliable performance of photovoltaic (PV) systems under partial shading conditions (PSCs) presents ongoing challenges, primarily due to the formation of multiple local maxima on the power-voltage (P-V) characteristic curve. These conditions often prevent conventional Maximum Power Point Tracking (MPPT) algorithms from locating the true global maximum. In recent years, nature-inspired metaheuristic approaches, particularly the Artificial Bee Colony (ABC) algorithm, have shown promising capabilities in navigating such complex optimization problems. This survey provides a comprehensive analysis of MPPT strategies under PSCs, with a focus on the ABC algorithm’s performance, adaptations, and integration into hybrid frameworks. The study systematically categorizes existing MPPT techniques, contrasts the performance of traditional and intelligent approaches, and critically assesses the role of the ABC algorithm in enhancing tracking accuracy, convergence speed, and energy harvesting efficiency. Furthermore, it explores various improvements and hybridizations of the ABC algorithm aimed at overcoming performance constraints in dynamic environmental conditions. By identifying existing research gaps and highlighting promising directions for future investigations, this survey provides valuable insights for researchers and practitioners seeking to optimize MPPT performance in real-world PV applications. The paper ultimately underscores the potential of the ABC algorithm as a robust and adaptive tool for next-generation solar energy systems operating under complex shading scenarios.