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Advisor(s)
Abstract(s)
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.
Description
Keywords
Wind turbine Machine learning Fault classification
Citation
Pinna, Danielle; Toso, Rodrigo; Coutinho, Rafaelli; Pereira, Ana I.; Brandão, Diego (2022). Fault identification in wind turbines: a data-centric machine learning approach. In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). p. 565-568. ISBN 979-8-3503-2028-2
Publisher
IEEE