Pinna, DanielleToso, RodrigoSemaan, GustavoSá, Fernando dePereira, Ana I.Ferreira, Ângela P.Soares, JorgeBrandão, Diego2024-10-082024-10-082024Pinna, 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 978-3-031-53035-7978-3-031-53035-7978-3-031-53036-4http://hdl.handle.net/10198/30377The 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.engWind turbineMachine learningFault classificationconference paper10.1007/978-3-031-53036-4_16