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Un algoritmo mejorado de optimización de manada de alces para el seguimiento del punto de máxima potencia en sistemas

Gang Zheng1, Wenchang Wei1, Heming Jia2

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El seguimiento tradicional del punto de potencia máxima (MPPT) tiene problemas con el sombreado parcial. Un algoritmo mejorado de optimización de manada de alces (IEHO) encuentra rápidamente el punto de potencia máxima global, aumentando la eficiencia del sistema fotovoltaico en condiciones variables.

Palabras clave:
Mejora de la optimización del rebaño de alcesseguimiento del punto de máxima potenciaCondición de sombreado parcialSistema fotovoltaico

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Área de la Ciencia:

  • Sistemas de energía renovable
  • Conversión de energía fotovoltaica
  • Algoritmos de optimización

Sus antecedentes:

  • Las condiciones de sombreado parcial (PSC) en los sistemas fotovoltaicos (PV) crean múltiples picos en las características de tensión de potencia.
  • Los algoritmos tradicionales de seguimiento del punto de máxima potencia (MPPT) a menudo quedan atrapados en óptimas locales, reduciendo la eficiencia de la conversión de energía.
  • La localización rápida y efectiva del punto de potencia máxima global en condiciones ambientales complejas es crucial para el rendimiento fotovoltaico.

Objetivo del estudio:

  • Proponer un algoritmo mejorado de optimización del rebaño de alces (IEHO) para el seguimiento rápido del punto de potencia máxima global (MPPT) en sistemas fotovoltaicos en diversas condiciones climáticas.
  • Mejorar el rendimiento del MPPT y la eficiencia de la conversión de energía en los sistemas fotovoltaicos que funcionan con sombreado parcial.

Principales métodos:

  • Desarrolló un algoritmo mejorado de optimización de manadas de alces (IEHO) que incorpora un mecanismo de actualización de posición guiado por la probabilidad de riesgo de depredación.
  • Introdujo una estrategia de caminata triangular para mejorar la capacidad del algoritmo para escapar del óptimo local.
  • Implementó una estrategia de redirección guiada por memoria para optimizar la velocidad de convergencia omitiendo los cálculos redundantes del ciclo de trabajo histórico.

Principales resultados:

  • El algoritmo IEHO demostró un rendimiento superior en comparación con otros algoritmos meta-heurísticos en varias condiciones climáticas.
  • Logró una eficiencia de seguimiento promedio del 99,99% en condiciones probadas.
  • Se alcanzó un tiempo de seguimiento promedio de 0,3886 segundos, lo que indica una mejora significativa en la velocidad de convergencia.

Conclusiones:

  • El algoritmo IEHO propuesto aborda efectivamente el desafío de la óptima local en MPPT bajo sombreado parcial.
  • IEHO mejora significativamente la velocidad y la precisión del seguimiento del punto de potencia máxima global en los sistemas fotovoltaicos.
  • El algoritmo ofrece una solución prometedora para mejorar la eficiencia general de conversión de energía de los sistemas fotovoltaicos en entornos dinámicos.