Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/55460
Título: Performance sensitivity of a wind farm power curve model to different signals of the input layer of ANNs: Case studies in the Canary Islands
Autores/as: Velázquez Medina, Sergio 
Carta González, José Antonio 
Portero Ajenjo, Ulises
Clasificación UNESCO: 332202 Generación de energía
Palabras clave: Wake
Optimization
Energy
Fecha de publicación: 2019
Publicación seriada: Complexity 
Resumen: Improving the estimation of the power output of a wind farm enables greater integration of this type of energy source in electrical systems. The development of accurate models that represent the real operation of a wind farm is one way to attain this objective. A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. Using such information could be of interest, either to study possible improvements that could be obtained in the performance of the original model, which uses exclusively the meteorological conditions of the area where the wind farm is located, or simply because no reliable meteorological data for the area of the wind farm are available. In the study developed it is deduced that the incorporation of meteorological data from an additional weather station other than that of the wind farm site can improve by up to 17.6% the performance of the original model.
URI: http://hdl.handle.net/10553/55460
ISSN: 1076-2787
DOI: 10.1155/2019/2869149
Fuente: Complexity [ISSN 1076-2787], v. 2019
Colección:Artículos
miniatura
pdf
Adobe PDF (3,57 MB)
Vista completa

Citas SCOPUSTM   

7
actualizado el 24-mar-2024

Citas de WEB OF SCIENCETM
Citations

5
actualizado el 25-feb-2024

Visitas

95
actualizado el 16-mar-2024

Descargas

107
actualizado el 16-mar-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.