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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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MECOA: Un algoritmo de optimización de coatíes mejorado con múltiples estrategias para la optimización global y la

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Biomimetics (Basel, Switzerland)
|December 24, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El Algoritmo de Optimización de Coatíes Mejorado con Múltiples Estrategias (MECOA) mejora la optimización global y la identificación de parámetros de modelos fotovoltaicos (FV). MECOA demuestra un rendimiento y eficiencia superiores a los métodos tradicionales en tareas de ingeniería complejas.

Palabras clave:
algoritmo de optimización de coatíesexploración-explotaciónoptimización globalestimación de parámetrosmodelos fotovoltaicos

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Área de la Ciencia:

  • Inteligencia Computacional
  • Algoritmos de Optimización
  • Sistemas de Energía Renovable

Sus antecedentes:

  • El Algoritmo de Optimización de Coatíes (COA) tradicional enfrenta limitaciones en la exploración global, la cooperación de la población y la eficiencia de la convergencia.
  • La identificación precisa de parámetros para modelos fotovoltaicos (FV) es crucial para la eficiencia y confiabilidad del sistema.

Objetivo del estudio:

  • Proponer un Algoritmo de Optimización de Coatíes Mejorado con Múltiples Estrategias (MECOA) para superar las limitaciones del COA tradicional.
  • Mejorar el rendimiento de COA para la optimización global y la identificación de parámetros de modelos fotovoltaicos.

Principales métodos:

  • MECOA incorpora búsqueda guiada por élites con vuelos de Lévy para una exploración-explotación equilibrada.
  • Se implementa el cruce horizontal para mejorar el intercambio de información y la eficiencia de la búsqueda cooperativa.
  • La estrategia de eliminación precisa elimina individuos de baja aptitud y genera nuevos alrededor de la mejor solución para mejorar la calidad de la población.

Principales resultados:

  • MECOA logró un rendimiento superior en los conjuntos de pruebas de referencia CEC2017 y CEC2022, superando a COA y otros algoritmos líderes.
  • El análisis estadístico confirmó la superioridad significativa de MECOA sobre COA.
  • Aplicado a modelos fotovoltaicos, MECOA redujo significativamente el RMSE para modelos de un solo diodo y logró una excelente precisión para modelos de doble diodo.

Conclusiones:

  • MECOA aborda eficazmente las limitaciones del COA tradicional.
  • El algoritmo propuesto demuestra un rendimiento robusto y eficiente en problemas complejos de optimización de ingeniería.
  • MECOA proporciona una solución confiable para la modelización y optimización precisas de sistemas fotovoltaicos.