MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms

A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very imp...

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Autores: Rabanal, Arkaitz, Ulazia Manterola, Alain, Ibarra Berastegi, Gabriel, Sáenz Aguirre, Jon, Elosegui, Unai
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/31864
Acceso en línea:http://hdl.handle.net/10810/31864
Access Level:acceso abierto
Palabra clave:wind turbine
anemometer
kernel-based multidimensional probability density function
ERA5 reanalysis
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spelling MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind FarmsRabanal, ArkaitzUlazia Manterola, AlainIbarra Berastegi, GabrielSáenz Aguirre, JonElosegui, Unaiwind turbineanemometerkernel-based multidimensional probability density functionERA5 reanalysisA novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and RMSE RMSE , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farmsThis work was financially supported by the Spanish Government through the MINECO project CGL2016-76561-R (MINECO/ERDF, UE), the University of the Basque Country through the Euskoiker PT10477 and GIU 17/002 contracts, and the project DIANEMOS of the Council of Gipuzkoa with Maxwind-Hispavista. ERA5 data were downloaded at no cost from the MARSserver of the ECMWF. Most of the calculations were carried out in the framework of RMDPI201920192018info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/31864reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MINECO/CGL2016-76561-R/https://www.mdpi.com/1996-1073/12/1/28info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Atribución 3.0 Españaoai:addi.ehu.eus:10810/318642026-06-18T09:23:17Z
dc.title.none.fl_str_mv MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
title MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
spellingShingle MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
Rabanal, Arkaitz
wind turbine
anemometer
kernel-based multidimensional probability density function
ERA5 reanalysis
title_short MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
title_full MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
title_fullStr MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
title_full_unstemmed MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
title_sort MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
dc.creator.none.fl_str_mv Rabanal, Arkaitz
Ulazia Manterola, Alain
Ibarra Berastegi, Gabriel
Sáenz Aguirre, Jon
Elosegui, Unai
author Rabanal, Arkaitz
author_facet Rabanal, Arkaitz
Ulazia Manterola, Alain
Ibarra Berastegi, Gabriel
Sáenz Aguirre, Jon
Elosegui, Unai
author_role author
author2 Ulazia Manterola, Alain
Ibarra Berastegi, Gabriel
Sáenz Aguirre, Jon
Elosegui, Unai
author2_role author
author
author
author
dc.subject.none.fl_str_mv wind turbine
anemometer
kernel-based multidimensional probability density function
ERA5 reanalysis
topic wind turbine
anemometer
kernel-based multidimensional probability density function
ERA5 reanalysis
description A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and RMSE RMSE , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farms
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/31864
url http://hdl.handle.net/10810/31864
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO/CGL2016-76561-R/
https://www.mdpi.com/1996-1073/12/1/28
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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