Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers

This article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS...

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Autores: Pérez Pérez, Esvan de Jesús, Puig Cayuela, Vicenç|||0000-0002-6364-6429, López Estrada, Francisco Ronay, Valencia Palomo, Guillermo, Santos Ruiz, Ildeberto, Samada Rigo, Sergio Emil
Tipo de recurso: artículo
Fecha de publicación:2023
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:2117/388009
Acceso en línea:https://hdl.handle.net/2117/388009
https://dx.doi.org/10.1016/j.ymssp.2023.110183
Access Level:acceso abierto
Palabra clave:Wind turbines
Wind turbine monitoring
Fault detection and isolation
Neuro-fuzzy network
qLPV systems
Zonotopic observer
ANFIS
Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observersPérez Pérez, Esvan de JesúsPuig Cayuela, Vicenç|||0000-0002-6364-6429López Estrada, Francisco RonayValencia Palomo, GuillermoSantos Ruiz, IldebertoSamada Rigo, Sergio EmilWind turbinesWind turbine monitoringFault detection and isolationNeuro-fuzzy networkqLPV systemsZonotopic observerANFISAerogeneradorsÀrees temàtiques de la UPC::Informàtica::Automàtica i controlThis article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS), which results in a set of qLPV polytopic models whose form is derived using structural analysis. Second, a bank of qLPV zonotopic observers is implemented to detect sensor and actuator faults. Unlike other works that consider different fault scenarios to train a neuronal network, in this work, only fault-free data is considered for the ANFIS. The FDI is based on the residual generation obtained by a bank of qLPV zonotopic observers of the identified models. Disturbances related to aerodynamic loads and measurement noise are considered to guarantee the robustness of the proposed method. The effectiveness of the proposed method is tested in a 5 MW WT well-known benchmark simulator based on fatigue, aerodynamics, structures, and turbulence under different fault scenarios.Peer Reviewed20232023-05-0120232023-05-29journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/388009https://dx.doi.org/10.1016/j.ymssp.2023.110183reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3880092026-05-27T15:37:01Z
dc.title.none.fl_str_mv Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
title Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
spellingShingle Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
Pérez Pérez, Esvan de Jesús
Wind turbines
Wind turbine monitoring
Fault detection and isolation
Neuro-fuzzy network
qLPV systems
Zonotopic observer
ANFIS
Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
title_full Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
title_fullStr Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
title_full_unstemmed Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
title_sort Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
dc.creator.none.fl_str_mv Pérez Pérez, Esvan de Jesús
Puig Cayuela, Vicenç|||0000-0002-6364-6429
López Estrada, Francisco Ronay
Valencia Palomo, Guillermo
Santos Ruiz, Ildeberto
Samada Rigo, Sergio Emil
author Pérez Pérez, Esvan de Jesús
author_facet Pérez Pérez, Esvan de Jesús
Puig Cayuela, Vicenç|||0000-0002-6364-6429
López Estrada, Francisco Ronay
Valencia Palomo, Guillermo
Santos Ruiz, Ildeberto
Samada Rigo, Sergio Emil
author_role author
author2 Puig Cayuela, Vicenç|||0000-0002-6364-6429
López Estrada, Francisco Ronay
Valencia Palomo, Guillermo
Santos Ruiz, Ildeberto
Samada Rigo, Sergio Emil
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Wind turbines
Wind turbine monitoring
Fault detection and isolation
Neuro-fuzzy network
qLPV systems
Zonotopic observer
ANFIS
Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Wind turbines
Wind turbine monitoring
Fault detection and isolation
Neuro-fuzzy network
qLPV systems
Zonotopic observer
ANFIS
Aerogeneradors
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description This article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS), which results in a set of qLPV polytopic models whose form is derived using structural analysis. Second, a bank of qLPV zonotopic observers is implemented to detect sensor and actuator faults. Unlike other works that consider different fault scenarios to train a neuronal network, in this work, only fault-free data is considered for the ANFIS. The FDI is based on the residual generation obtained by a bank of qLPV zonotopic observers of the identified models. Disturbances related to aerodynamic loads and measurement noise are considered to guarantee the robustness of the proposed method. The effectiveness of the proposed method is tested in a 5 MW WT well-known benchmark simulator based on fatigue, aerodynamics, structures, and turbulence under different fault scenarios.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-01
2023
2023-05-29
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/2117/388009
https://dx.doi.org/10.1016/j.ymssp.2023.110183
url https://hdl.handle.net/2117/388009
https://dx.doi.org/10.1016/j.ymssp.2023.110183
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-NoDerivatives 4.0 International
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
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score 15,300719

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