Browsing by Author "Pinna, Danielle"
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- Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization MethodsPublication . Pinna, Danielle; Toso, Rodrigo; Semaan, Gustavo; Sá, Fernando de; Pereira, Ana I.; Ferreira, Ângela P.; Soares, Jorge; Brandão, DiegoThe 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.
- Fault identification in wind turbines: a data-centric machine learning approachPublication . Pinna, Danielle; Toso, Rodrigo; Coutinho, Rafaelli; Pereira, Ana I.; Brandão, DiegoThe last few years have been marked by the transition of the world energy matrix, predominantly with wind and solar sources considered clean energies. Wind turbines, responsible for the energy conversion process, are complex and expensive equipment susceptible to several failures due to multiple factors. Monitoring turbine components can assist in detecting failures before they occur, reducing equipment maintenance costs. This work compares machine learning techniques in a data-centric approach to wind turbine failure detection. Preliminary results demonstrate the importance of feature selection in this problem.