Investigating machine learning capabilities for fault classification and fault region identification in onshore wind farm collectors (2025)
- Authors:
- USP affiliated authors: OLESKOVICZ, MARIO - EESC ; VIEIRA JÚNIOR, JOSÉ CARLOS DE MELO - EESC ; CUNHA, TALITA MITSUE ONOSE ARAUJO - EESC ; SILVA, MAURÍCIO PAVANI DA - EESC ; DAVI, MOISÉS JUNIOR BATISTA BORGES - EESC
- Unidade: EESC
- DOI: 10.1109/PowerTech59965.2025.11180434
- 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 assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
DAVI, Moisés Junior Batista Borges et al. Investigating machine learning capabilities for fault classification and fault region identification in onshore 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/PowerTech59965.2025.11180434. Acesso em: 03 dez. 2025. -
APA
Davi, M. J. B. B., Silva, M. P. da, Lopes, G. N., Cunha, T. M. O. A., Oleskovicz, M., & Vieira Júnior, J. C. de M. (2025). Investigating machine learning capabilities for fault classification and fault region identification in onshore wind farm collectors. In . Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/PowerTech59965.2025.11180434 -
NLM
Davi MJBB, Silva MP da, Lopes GN, Cunha TMOA, Oleskovicz M, Vieira Júnior JC de M. Investigating machine learning capabilities for fault classification and fault region identification in onshore wind farm collectors [Internet]. 2025 ;[citado 2025 dez. 03 ] Available from: http://dx.doi.org/10.1109/PowerTech59965.2025.11180434 -
Vancouver
Davi MJBB, Silva MP da, Lopes GN, Cunha TMOA, Oleskovicz M, Vieira Júnior JC de M. Investigating machine learning capabilities for fault classification and fault region identification in onshore wind farm collectors [Internet]. 2025 ;[citado 2025 dez. 03 ] Available from: http://dx.doi.org/10.1109/PowerTech59965.2025.11180434 - Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines
- Insights and recommendations for the assessment and design of fault diagnosis methods applied to modern distribution systems
- Exploring machine learning-based solutions for fault classification and region identification in onshore wind farm collector systems
- Exploring machine learning techniques for fault classification in wind farm collectors
- Insights and challenges on the protection of grid-forming converter interconnection lines
- Impacts of advances in standards for inverter-based resources controls on distance protection
- Pseudo-incremental normalized quantity-based phase selection method for systems with conventional and inverter-based resources
- Maturity analysis of protection solutions for power systems near inverter-based resources
- An impedance-multi-method-based fault location methodology for transmission lines connected to inverter-based resources
- A review of signal processing for fault diagnosis in systems with inverter-based resources and an improved high-frequency component-based disturbance detector
Informações sobre o DOI: 10.1109/PowerTech59965.2025.11180434 (Fonte: oaDOI API)
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