Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods

Pinna, Danielle; Toso, Rodrigo; Semaan, Gustavo; Sá, Fernando de; Pereira, Ana I.; Ferreira, Ângela P.; Soares, Jorge; Brandão, Diego

Publication

Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods

2024Conference paper

dc.contributor.author	Pinna, Danielle
dc.contributor.author	Toso, Rodrigo
dc.contributor.author	Semaan, Gustavo
dc.contributor.author	Sá, Fernando de
dc.contributor.author	Pereira, Ana I.
dc.contributor.author	Ferreira, Ângela P.
dc.contributor.author	Soares, Jorge
dc.contributor.author	Brandão, Diego
dc.date.accessioned	2024-10-08T13:57:20Z
dc.date.available	2024-10-08T13:57:20Z
dc.date.issued	2024
dc.description.abstract	The last few years have been marked by the insertion of renewable technologies in the global energy matrix, such as wind and solar energy, which are considered clean energies with low environmental impact. Wind turbines, responsible for the energy conversion process, are complex equipment that are expensive and susceptible to numerous failures. Monitoring turbine components can help detect failures before they occur, reducing equipment maintenance costs. This work compares the training time of different techniques for tuning hyperparameters in supervised machine-learning models for fault detection in wind turbines. Results show the importance of data optimization during model training.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Pinna, Danielle; Toso, Rodrigo; Semaan, Gustavo; Sá, Fernando de; Pereira, Ana I.; Ferreira, Ângela; Soares, Jorge; Brandão, Diego (2024). Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 229–243. ISBN 978-3-031-53035-7	pt_PT
dc.identifier.doi	10.1007/978-3-031-53036-4_16	pt_PT
dc.identifier.isbn	978-3-031-53035-7
dc.identifier.isbn	978-3-031-53036-4
dc.identifier.uri	http://hdl.handle.net/10198/30377
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer Nature	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Wind turbine	pt_PT
dc.subject	Machine learning	pt_PT
dc.subject	Fault classification	pt_PT
dc.title	Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods	pt_PT
dc.type	conference paper
dspace.entity.type	Publication
oaire.citation.endPage	243	pt_PT
oaire.citation.startPage	229	pt_PT
oaire.citation.title	3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)	pt_PT
person.familyName	Pereira
person.familyName	Ferreira
person.givenName	Ana I.
person.givenName	Ângela P.
person.identifier.ciencia-id	0716-B7C2-93E4
person.identifier.ciencia-id	2211-6787-D936
person.identifier.orcid	0000-0003-3803-2043
person.identifier.orcid	0000-0002-1912-2556
person.identifier.rid	F-3168-2010
person.identifier.rid	M-8188-2013
person.identifier.scopus-author-id	15071961600
person.identifier.scopus-author-id	55516840300
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