A Survey of Artificial Neural Network in Wind Energy Systems

Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifyin...

Descripción completa

Detalles Bibliográficos
Autores: Pliego Marugán, Alberto, García Márquez, Fausto Pedro, Pinar Pérez, Jesús María, Ruíz Hernández, Diego
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/18939
Acceso en línea:https://doi.org/10.1016/j.apenergy.2018.07.084
https://hdl.handle.net/10578/18939
Access Level:acceso abierto
Palabra clave:Artificial neural networks
Wind energy systems
Wind turbine
id ES_fcef8a19730d2f06d702de3a04927bea
oai_identifier_str oai:ruidera.uclm.es:10578/18939
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Survey of Artificial Neural Network in Wind Energy SystemsPliego Marugán, AlbertoGarcía Márquez, Fausto PedroPinar Pérez, Jesús MaríaRuíz Hernández, DiegoArtificial neural networksWind energy systemsWind turbineWind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies.Elsevier201820182018info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1016/j.apenergy.2018.07.084https://hdl.handle.net/10578/18939reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésDPI2015- 67264-Pinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/189392026-05-27T07:36:41Z
dc.title.none.fl_str_mv A Survey of Artificial Neural Network in Wind Energy Systems
title A Survey of Artificial Neural Network in Wind Energy Systems
spellingShingle A Survey of Artificial Neural Network in Wind Energy Systems
Pliego Marugán, Alberto
Artificial neural networks
Wind energy systems
Wind turbine
title_short A Survey of Artificial Neural Network in Wind Energy Systems
title_full A Survey of Artificial Neural Network in Wind Energy Systems
title_fullStr A Survey of Artificial Neural Network in Wind Energy Systems
title_full_unstemmed A Survey of Artificial Neural Network in Wind Energy Systems
title_sort A Survey of Artificial Neural Network in Wind Energy Systems
dc.creator.none.fl_str_mv Pliego Marugán, Alberto
García Márquez, Fausto Pedro
Pinar Pérez, Jesús María
Ruíz Hernández, Diego
author Pliego Marugán, Alberto
author_facet Pliego Marugán, Alberto
García Márquez, Fausto Pedro
Pinar Pérez, Jesús María
Ruíz Hernández, Diego
author_role author
author2 García Márquez, Fausto Pedro
Pinar Pérez, Jesús María
Ruíz Hernández, Diego
author2_role author
author
author
dc.subject.none.fl_str_mv Artificial neural networks
Wind energy systems
Wind turbine
topic Artificial neural networks
Wind energy systems
Wind turbine
description Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.apenergy.2018.07.084
https://hdl.handle.net/10578/18939
url https://doi.org/10.1016/j.apenergy.2018.07.084
https://hdl.handle.net/10578/18939
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv DPI2015- 67264-P
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425494956441600
score 15,301629

Similares en LA Referencia