Comparative Evaluation of Metaheuristic MPPT Algorithms for PV Systems Under Partial Shading Conditions

Enhancing the energy output of photovoltaic (PV) systems is essential due to their inherently limited efficiency. Consequently, maximum power point tracking (MPPT) techniques have become a crucial component in photovoltaic systems for improving energy harvesting efficiency. However, conventional MPPT methods often require accurate mathematical modeling of PV systems, which remains a significant challenge due to their highly nonlinear behavior. To address this issue, researchers have proposed various MPPT strategies. The complex nonlinear behavior of PV systems makes pattern extraction difficult, often leading to simplified assumptions that may reduce tracking accuracy and overall system performance. This study investigates the performance of five metaheuristic optimization algorithms for MPPT under partial shading conditions (PSC) to improve PV system efficiency. The considered algorithms include Particle Swarm Optimization Algorithm (PSOA), Grey Wolf Optimization Algorithm (GWOA), Cuckoo Search Optimization Algorithm (CSOA), Genetic Algorithm (GA), and Quantum-Inspired Evolutionary Algorithm (QIEA). Although several studies have investigated metaheuristic MPPT techniques under partial shading conditions, relatively limited research has provided a comprehensive comparative evaluation of swarm-based, evolutionary-based, and physics-inspired optimization approaches considering tracking efficiency and dynamic response characteristics. The results demonstrate that the physics-inspired QIEA achieves a superior balance between exploration and exploitation, thereby enhancing its ability to accurately identify the global maximum power point.

کلیدواژه ها:

Photovoltaic system ، maximum power point tracking ، Meta-heuristic optimization algorithm ، Partial shading conditions