A dandelion optimization-based MPPT control technique for solar PV system under partial shading conditions

Solar photovoltaic (PV) systems require maximum power point tracking (MPPT) algorithms to ensure efficient energy harvesting. However, their performance deteriorates under partial shading conditions (PSC) due to non-uniform irradiance, temperature fluctuations, and other environmental factors. This paper proposes a novel MPPT method based on the Dandelion Optimization (DO) algorithm—a nature-inspired metaheuristic technique—designed to accurately identify the global maximum power point (GMPP) under PSC. The proposed DO-MPPT algorithm is evaluated through simulations under various shading scenarios and compared against four established algorithms: Perturb and Observe (P&O), Particle Swarm Optimization (PSO), Dragonfly Algorithm (DFO), and Adaptive Cuckoo Search (ACS). Results show that the DO algorithm achieves a convergence time of 0.2 s, a settling time of 0.23 s, and a tracking efficiency of 99.9 %, surpassing the alternatives in speed, accuracy, and stability. Furthermore, it minimizes voltage oscillations and reliably tracks GMPP across diverse operating conditions. These findings demonstrate the effectiveness of the DO algorithm in enhancing PV system performance under challenging real-world environments.