Assessing the Efficacy of optimization algorithms, P&O and MPC in MPPT for Photovoltaic Systems: A Comparative Study - Mena-Palomeque, Jose; Zabala Daza, Juan Esteban; Gomez Pemberty, Jhon Anderson; Ramirez Ospina, Jaime Alberto (Ingeniería, 2026)
Context: The need for efficient energy harvesting in photovoltaic (PV) systems requires advanced maximum power point tracking (MPPT) techniques. Traditional methods like perturb and observe (P&O) exhibit limitations under varying conditions, leading to less efficient energy extraction. Method: This study compares a model-predictive controller (MPC) combined with optimization algorithms such as the particle swarm optimizer (PSO), the vortex search algorithm (VSA), and the salp swarm algorithm (SSA) to improve MPPT performance in PV systems. These algorithms are tested under different irradiance conditions to assess their efficiency and adaptability. Results: The results show that PSO, when used with MPC, greatly enhances energy extraction compared to the conventional P&O method. Additionally, VSA and SSA also perform well in adapting to rapid irradiance changes, ensuring an optimal power output. Conclusions: Optimization-based MPPT methods, especially those using PSO, offer significant efficiency improvements for PV systems, making them strong alternatives to traditional techniques.