Exploring machine learning techniques for fault classification in wind farm collectors (2025)
- Authors:
- USP affiliated authors: OLESKOVICZ, MARIO - EESC ; CUNHA, TALITA MITSUE ONOSE ARAUJO - EESC ; GRILO, CAIO VINÍCIUS COLOZZO - EESC ; DAVI, MOISÉS JUNIOR BATISTA BORGES - EESC
- Unidade: EESC
- DOI: 10.1109/PowerTech59965.2025.11180214
- Assunto: ENGENHARIA ELÉTRICA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway, NJ, USA
- Date published: 2025
- Conference titles: IEEE Kiel PowerTech - PowerTech 2025
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
CUNHA, Talita Mitsue Onose Araujo et al. Exploring machine learning techniques for fault classification in wind farm collectors. 2025, Anais.. Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2025. Disponível em: http://dx.doi.org/10.1109/PowerTech59965.2025.11180214. Acesso em: 13 fev. 2026. -
APA
Cunha, T. M. O. A., Grilo, C. V. C., Davi, M. J. B. B., & Oleskovicz, M. (2025). Exploring machine learning techniques for fault classification in wind farm collectors. In . Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/PowerTech59965.2025.11180214 -
NLM
Cunha TMOA, Grilo CVC, Davi MJBB, Oleskovicz M. Exploring machine learning techniques for fault classification in wind farm collectors [Internet]. 2025 ;[citado 2026 fev. 13 ] Available from: http://dx.doi.org/10.1109/PowerTech59965.2025.11180214 -
Vancouver
Cunha TMOA, Grilo CVC, Davi MJBB, Oleskovicz M. Exploring machine learning techniques for fault classification in wind farm collectors [Internet]. 2025 ;[citado 2026 fev. 13 ] Available from: http://dx.doi.org/10.1109/PowerTech59965.2025.11180214 - Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines
- Exploring machine learning-based solutions for fault classification and region identification in onshore wind farm collector systems
- An improved methodology to locate faults in onshore wind farm collector systems
- Insights and challenges on the protection of grid-forming converter interconnection lines
- Assessment of traveling wave-based functions in inverter-based resource interconnecting lines
- A review of signal processing for fault diagnosis in systems with inverter-based resources and an improved high-frequency component-based disturbance detector
- Effects of system stray capacitances on overcurrent and directional traveling wave protections applied to lines near inverter-based resources
- Classificação de faltas em linhas de interconexão de gerações baseadas em inversores: desvendando o potencial dos métodos de machine learning
- Maturity analysis of protection solutions for power systems near inverter-based resources
- Exploring the potential of a machine learning-based methodology for fault classification in inverter-based resource interconnection lines
Informações sobre o DOI: 10.1109/PowerTech59965.2025.11180214 (Fonte: oaDOI API)
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