Wind turbine database for intelligent operation and maintenance strategies

With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recor...

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Detalles Bibliográficos
Autores: Blanco Martínez, Alejandro, Martí i Puig, Pere, Cusidó, Jordi, Solé-Casals, Jordi
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:UVic-UCC
Repositorio:RiUVic. Repositori institucional de la UVic-UCC
OAI Identifier:oai:dspace.uvic.cat:10854/180301
Acceso en línea:http://hdl.handle.net/10854/180301
https://doi.org/10.1038/s41597-024-03067-9
Access Level:acceso abierto
Palabra clave:Energia eòlica
Parcs eòlics -- Manteniment i reparació
Turbines
Aerogeneradors
62
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oai_identifier_str oai:dspace.uvic.cat:10854/180301
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repository_id_str
spelling Wind turbine database for intelligent operation and maintenance strategiesBlanco Martínez, AlejandroMartí i Puig, PereCusidó, JordiSolé-Casals, JordiEnergia eòlicaParcs eòlics -- Manteniment i reparacióTurbinesAerogeneradors62With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value.info:eu-repo/semantics/publishedVersionSpringer NatureUniversitat de Vic - Universitat Central de Catalunya. Grup de Recerca en Tractament de Dades i senyalsUniversitat Politècnica de Catalunya2025202520252024info:eu-repo/semantics/article13 p.application/pdfhttp://hdl.handle.net/10854/180301https://doi.org/10.1038/s41597-024-03067-9reponame:RiUVic. Repositori institucional de la UVic-UCCinstname:UVic-UCCInglésAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dspace.uvic.cat:10854/1803012026-06-07T19:15:21Z
dc.title.none.fl_str_mv Wind turbine database for intelligent operation and maintenance strategies
title Wind turbine database for intelligent operation and maintenance strategies
spellingShingle Wind turbine database for intelligent operation and maintenance strategies
Blanco Martínez, Alejandro
Energia eòlica
Parcs eòlics -- Manteniment i reparació
Turbines
Aerogeneradors
62
title_short Wind turbine database for intelligent operation and maintenance strategies
title_full Wind turbine database for intelligent operation and maintenance strategies
title_fullStr Wind turbine database for intelligent operation and maintenance strategies
title_full_unstemmed Wind turbine database for intelligent operation and maintenance strategies
title_sort Wind turbine database for intelligent operation and maintenance strategies
dc.creator.none.fl_str_mv Blanco Martínez, Alejandro
Martí i Puig, Pere
Cusidó, Jordi
Solé-Casals, Jordi
author Blanco Martínez, Alejandro
author_facet Blanco Martínez, Alejandro
Martí i Puig, Pere
Cusidó, Jordi
Solé-Casals, Jordi
author_role author
author2 Martí i Puig, Pere
Cusidó, Jordi
Solé-Casals, Jordi
author2_role author
author
author
dc.contributor.none.fl_str_mv Universitat de Vic - Universitat Central de Catalunya. Grup de Recerca en Tractament de Dades i senyals
Universitat Politècnica de Catalunya
dc.subject.none.fl_str_mv Energia eòlica
Parcs eòlics -- Manteniment i reparació
Turbines
Aerogeneradors
62
topic Energia eòlica
Parcs eòlics -- Manteniment i reparació
Turbines
Aerogeneradors
62
description With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10854/180301
https://doi.org/10.1038/s41597-024-03067-9
url http://hdl.handle.net/10854/180301
https://doi.org/10.1038/s41597-024-03067-9
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13 p.
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:RiUVic. Repositori institucional de la UVic-UCC
instname:UVic-UCC
instname_str UVic-UCC
reponame_str RiUVic. Repositori institucional de la UVic-UCC
collection RiUVic. Repositori institucional de la UVic-UCC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414478489059328
score 15.81155

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