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Este estudio presenta una estrategia jerárquica de fusión de datos para una medición precisa de la velocidad del viento utilizando múltiples anemómetros. El método mejora significativamente la calidad de los datos y la eficiencia del procesamiento para aplicaciones eólicas y meteorológicas.

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Área de la Ciencia:

  • Ingeniería
  • Ciencia de Datos
  • Ciencias Ambientales

Sus antecedentes:

  • La medición precisa de la velocidad del viento es crucial para la generación de energía eólica y la monitorización meteorológica.
  • La fusión de datos multisensores de anemómetros en torres eólicas es un método principal para datos de alta precisión de la velocidad del viento.
  • Los métodos de fusión existentes enfrentan desafíos en calidad y eficiencia.

Objetivo del estudio:

  • Proponer una estrategia jerárquica de fusión de datos para mejorar la calidad y la eficiencia de la fusión de datos de velocidad del viento multisensores.
  • Mejorar la precisión y la velocidad del procesamiento de datos en sistemas de medición de viento.

Principales métodos:

  • Un enfoque de fusión en dos etapas: fusión local utilizando un filtro de Kalman sin perfume (FLR-UKF) mejorado con lógica difusa y factor de robustez para la eliminación de ruido y la fusión.
  • Fusión global utilizando una máquina de aprendizaje extremo (ELM) optimizada por un optimizador de águila mejorado con aprendizaje por refuerzo (QLIAO-ELM) con capacidades de búsqueda mejoradas.

Principales resultados:

  • El FLR-UKF redujo el Error Cuadrático Medio (RMSE) en un 26,46 %–28,6 % en comparación con los filtros de Kalman sin perfume (UKF) tradicionales.
  • El QLIAO-ELM logró reducciones de RMSE del 27,1 % y del 14,0 % en comparación con el ELM estándar y el ISSA-ELM, respectivamente.
  • El método propuesto demostró una mayor precisión y eficiencia en la fusión de datos de velocidad del viento.

Conclusiones:

  • La estrategia jerárquica de fusión de datos mejora eficazmente tanto la precisión como la eficiencia de las mediciones de velocidad del viento multisensores.
  • Los novedosos métodos FLR-UKF y QLIAO-ELM ofrecen mejoras significativas sobre las técnicas existentes para el procesamiento de datos de viento.
  • Este enfoque proporciona una solución confiable para información de alta precisión de la velocidad del viento, vital para las energías renovables y la meteorología.