Effects of the pre-processing algorithms in fault diagnosis of wind turbines

The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on t...

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Autores: Marti-Puig, Pere|||0000-0001-6582-4551, Blanco Martínez, Alejandro, Cárdenas Araújo, Juan José, Cusidó Roura, Jordi|||0000-0002-1951-1498, Sole Casals, Jordi
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/336039
Acesso em linha:https://hdl.handle.net/2117/336039
https://dx.doi.org/10.1016/j.envsoft.2018.05.002
Access Level:acceso abierto
Palavra-chave:System failures (Engineering)
Wind turbines--Maintenance and repair
Machine learning
Supervisory control systems
Wind farms
SCADA data
Pre-processing
Outliers
Fault diagnosis
Renewable energy
Aerogeneradors -- Manteniment i reparació
Avaries
Aprenentatge automatic
Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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oai_identifier_str oai:upcommons.upc.edu:2117/336039
network_acronym_str ES
network_name_str España
repository_id_str
spelling Effects of the pre-processing algorithms in fault diagnosis of wind turbinesMarti-Puig, Pere|||0000-0001-6582-4551Blanco Martínez, AlejandroCárdenas Araújo, Juan JoséCusidó Roura, Jordi|||0000-0002-1951-1498Sole Casals, JordiSystem failures (Engineering)Wind turbines--Maintenance and repairMachine learningSupervisory control systemsWind farmsSCADA dataPre-processingOutliersFault diagnosisRenewable energyAerogeneradors -- Manteniment i reparacióAvariesAprenentatge automaticÀrees temàtiques de la UPC::Energies::Energia eòlica::AerogeneradorsÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThe wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.Financial support by the Agency for Management of University and Research Grants (AGAUR) of the Catalan Government to Alejandro Blanco-M. is gratefully acknowledged.Peer ReviewedElsevier20182018-12-0120212021-01-26journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/336039https://dx.doi.org/10.1016/j.envsoft.2018.05.002reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 4.0http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3360392026-05-27T15:37:01Z
dc.title.none.fl_str_mv Effects of the pre-processing algorithms in fault diagnosis of wind turbines
title Effects of the pre-processing algorithms in fault diagnosis of wind turbines
spellingShingle Effects of the pre-processing algorithms in fault diagnosis of wind turbines
Marti-Puig, Pere|||0000-0001-6582-4551
System failures (Engineering)
Wind turbines--Maintenance and repair
Machine learning
Supervisory control systems
Wind farms
SCADA data
Pre-processing
Outliers
Fault diagnosis
Renewable energy
Aerogeneradors -- Manteniment i reparació
Avaries
Aprenentatge automatic
Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Effects of the pre-processing algorithms in fault diagnosis of wind turbines
title_full Effects of the pre-processing algorithms in fault diagnosis of wind turbines
title_fullStr Effects of the pre-processing algorithms in fault diagnosis of wind turbines
title_full_unstemmed Effects of the pre-processing algorithms in fault diagnosis of wind turbines
title_sort Effects of the pre-processing algorithms in fault diagnosis of wind turbines
dc.creator.none.fl_str_mv Marti-Puig, Pere|||0000-0001-6582-4551
Blanco Martínez, Alejandro
Cárdenas Araújo, Juan José
Cusidó Roura, Jordi|||0000-0002-1951-1498
Sole Casals, Jordi
author Marti-Puig, Pere|||0000-0001-6582-4551
author_facet Marti-Puig, Pere|||0000-0001-6582-4551
Blanco Martínez, Alejandro
Cárdenas Araújo, Juan José
Cusidó Roura, Jordi|||0000-0002-1951-1498
Sole Casals, Jordi
author_role author
author2 Blanco Martínez, Alejandro
Cárdenas Araújo, Juan José
Cusidó Roura, Jordi|||0000-0002-1951-1498
Sole Casals, Jordi
author2_role author
author
author
author
dc.subject.none.fl_str_mv System failures (Engineering)
Wind turbines--Maintenance and repair
Machine learning
Supervisory control systems
Wind farms
SCADA data
Pre-processing
Outliers
Fault diagnosis
Renewable energy
Aerogeneradors -- Manteniment i reparació
Avaries
Aprenentatge automatic
Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic System failures (Engineering)
Wind turbines--Maintenance and repair
Machine learning
Supervisory control systems
Wind farms
SCADA data
Pre-processing
Outliers
Fault diagnosis
Renewable energy
Aerogeneradors -- Manteniment i reparació
Avaries
Aprenentatge automatic
Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-12-01
2021
2021-01-26
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/336039
https://dx.doi.org/10.1016/j.envsoft.2018.05.002
url https://hdl.handle.net/2117/336039
https://dx.doi.org/10.1016/j.envsoft.2018.05.002
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-NonCommercial-NoDerivs 4.0
http://creativecommons.org/licenses/by-nc-nd/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-NonCommercial-NoDerivs 4.0
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1869402654986207232
score 15,301629

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