Fonte: Proceedings. Nome do evento: International Conference on Harmonics and Quality of Power - ICHQP. Unidades: EESC, EESC E ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, FALHA, FONTES RENOVÁVEIS DE ENERGIA, ENGENHARIA ELÉTRICA
ABNT
DAVI, Moisés Junior Batista Borges et al. Exploring machine learning-based solutions for fault classification and region identification in onshore wind farm collector systems. 2024, Anais.. Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2024. Disponível em: http://dx.doi.org/10.1109/ICHQP61174.2024.10768736. Acesso em: 25 nov. 2025.APA
Davi, M. J. B. B., Cunha, T. M. O. A., Oliveira, E. P. G. de, & Oleskovicz, M. (2024). Exploring machine learning-based solutions for fault classification and region identification in onshore wind farm collector systems. In Proceedings. Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/ICHQP61174.2024.10768736NLM
Davi MJBB, Cunha TMOA, Oliveira EPG de, Oleskovicz M. Exploring machine learning-based solutions for fault classification and region identification in onshore wind farm collector systems [Internet]. Proceedings. 2024 ;[citado 2025 nov. 25 ] Available from: http://dx.doi.org/10.1109/ICHQP61174.2024.10768736Vancouver
Davi MJBB, Cunha TMOA, Oliveira EPG de, Oleskovicz M. Exploring machine learning-based solutions for fault classification and region identification in onshore wind farm collector systems [Internet]. Proceedings. 2024 ;[citado 2025 nov. 25 ] Available from: http://dx.doi.org/10.1109/ICHQP61174.2024.10768736

