Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems

In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our...

Descripción completa

Detalles Bibliográficos
Autores: Acho Zuppa, Leonardo|||0000-0002-4965-1133, Pujol Vázquez, Gisela|||0000-0003-0067-2571
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/359179
Acceso en línea:https://hdl.handle.net/2117/359179
https://dx.doi.org/10.3390/s21248437
Access Level:acceso abierto
Palabra clave:Wind turbines
Fault tolerance (Engineering)
Data fusion
Iterative learning
Fault detection
Pitch system
Aerogeneradors
Tolerància als errors (Enginyeria)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
id ES_5f8010c55fa84d280f62ecb9d30b3c8c
oai_identifier_str oai:upcommons.upc.edu:2117/359179
network_acronym_str ES
network_name_str España
repository_id_str
spelling Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systemsAcho Zuppa, Leonardo|||0000-0002-4965-1133Pujol Vázquez, Gisela|||0000-0003-0067-2571Wind turbinesFault tolerance (Engineering)Data fusionIterative learningFault detectionPitch systemWind turbinesAerogeneradorsTolerància als errors (Enginyeria)Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciènciesIn this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20212021-12-2020212021-12-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/359179https://dx.doi.org/10.3390/s21248437reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3591792026-05-27T15:37:01Z
dc.title.none.fl_str_mv Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
title Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
spellingShingle Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
Acho Zuppa, Leonardo|||0000-0002-4965-1133
Wind turbines
Fault tolerance (Engineering)
Data fusion
Iterative learning
Fault detection
Pitch system
Wind turbines
Aerogeneradors
Tolerància als errors (Enginyeria)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
title_short Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
title_full Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
title_fullStr Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
title_full_unstemmed Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
title_sort Data fusion based on an iterative learning algorithm for fault detection in wind turbine pitch control systems
dc.creator.none.fl_str_mv Acho Zuppa, Leonardo|||0000-0002-4965-1133
Pujol Vázquez, Gisela|||0000-0003-0067-2571
author Acho Zuppa, Leonardo|||0000-0002-4965-1133
author_facet Acho Zuppa, Leonardo|||0000-0002-4965-1133
Pujol Vázquez, Gisela|||0000-0003-0067-2571
author_role author
author2 Pujol Vázquez, Gisela|||0000-0003-0067-2571
author2_role author
dc.subject.none.fl_str_mv Wind turbines
Fault tolerance (Engineering)
Data fusion
Iterative learning
Fault detection
Pitch system
Wind turbines
Aerogeneradors
Tolerància als errors (Enginyeria)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
topic Wind turbines
Fault tolerance (Engineering)
Data fusion
Iterative learning
Fault detection
Pitch system
Wind turbines
Aerogeneradors
Tolerància als errors (Enginyeria)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
description In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-12-20
2021
2021-12-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/359179
https://dx.doi.org/10.3390/s21248437
url https://hdl.handle.net/2117/359179
https://dx.doi.org/10.3390/s21248437
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409211538997248
score 15,300724

Similares en LA Referencia