Autor(es): Pinna, Danielle ; Toso, Rodrigo ; Coutinho, Rafaelli ; Pereira, Ana I. ; Brandão, Diego
Data: 2022
Identificador Persistente: http://hdl.handle.net/10198/29263
Origem: Biblioteca Digital do IPB
Assunto(s): Wind turbine; Machine learning; Fault classification
The 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.
