DOI:
https://doi.org/10.14483/23448393.23090Published:
2026-04-08Issue:
Vol. 31 No. 1 (2026)Section:
Electrical, Electronic and Telecommunications EngineeringAssessing the Efficacy of optimization algorithms, P&O and MPC in MPPT for Photovoltaic Systems: A Comparative Study
Evaluación de la eficacia de los algoritmos de optimización, P&O y MPC en MPPT para sistemas fotovoltaicos: Un estudio comparativo
Keywords:
Solar power system, Partial Shading, Power converter, DC/DC converters, Boost converter, maximum power point tracking, MPPT, MPC, model predictive control, Optimization algorithms, Energy effiency (en).Keywords:
Sistemas de Energía solar, Sombreado parcial, Convertidores de potencia, convertidores DC/DC, convertidor Boost, Seguimiento de punto de máxima potencia, MPPT, MPC, Control Predictivo Basado En Modelo, Algoritmos de optimización, Eficiencia Energetica (es).Downloads
Abstract (en)
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.
Abstract (es)
Contexto: La creciente demanda de extracción eficiente de energía en sistemas fotovoltaicos (PV) requiere técnicas avanzadas de seguimiento del punto de máxima potencia (MPPT). Métodos tradicionales como perturb and observe (P&O) enfrentan limitaciones bajo condiciones ambientales variables, conlleva una extracción subóptima de energía.
Método: Este estudio presenta un análisis comparativo de un controlador predictivo de modelos (MPC) integrado con varios algoritmos de optimización, incluyendo la Optimización por Enjambre de Partículas (PSO), el Algoritmo de Búsqueda de Vórtices (VSA) y el Algoritmo de Enjambre de Salpas (SSA), para mejorar el rendimiento del MPPT en sistemas PV. Los algoritmos fueron evaluados bajo diferentes perfiles de irradiancia para determinar su eficiencia y adaptabilidad.
Resultados: Los resultados indican que PSO, cuando se combina con MPC, mejora significativamente la eficiencia de extracción de energía en comparación con el método convencional P&O. Además, VSA y SSA también demuestran un rendimiento superior al adaptarse a cambios rápidos en la irradiancia, asegurando una salida de potencia optima.
Conclusiones: Los métodos MPPT basados en optimización, particularmente aquellos que utilizan PSO, ofrecen mejoras sustanciales en la eficiencia de los sistemas PV, lo que los posiciona como alternativas viables a las técnicas MPPT tradicionales.
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Copyright (c) 2026 JOSE MENA PALOMEQUE, Juan Esteban Zabala Daza, Jhon Anderson Gomez Pemberty, Jaime Alberto Ramirez Ospina
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