Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines (2024)
- Authors:
- USP affiliated authors: OLESKOVICZ, MARIO - EESC ; CUNHA, TALITA MITSUE ONOSE ARAUJO - EESC ; DAVI, MOISÉS JUNIOR BATISTA BORGES - EESC
- Unidade: EESC
- DOI: 10.1109/ICHQP61174.2024.10768802
- Subjects: PROTEÇÃO DE SISTEMAS ELÉTRICOS; APRENDIZADO COMPUTACIONAL; FALHA; FONTES RENOVÁVEIS DE ENERGIA; ENGENHARIA ELÉTRICA
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway, NJ, USA
- Date published: 2024
- Source:
- Título: Proceedings
- Conference titles: International Conference on Harmonics and Quality of Power - ICHQP
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CUNHA, Talita Mitsue Onose Araujo e DAVI, Moisés Junior Batista Borges e OLESKOVICZ, Mario. Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines. 2024, Anais.. Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2024. Disponível em: http://dx.doi.org/10.1109/ICHQP61174.2024.10768802. Acesso em: 28 dez. 2025. -
APA
Cunha, T. M. O. A., Davi, M. J. B. B., & Oleskovicz, M. (2024). Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines. In Proceedings. Piscataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/ICHQP61174.2024.10768802 -
NLM
Cunha TMOA, Davi MJBB, Oleskovicz M. Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines [Internet]. Proceedings. 2024 ;[citado 2025 dez. 28 ] Available from: http://dx.doi.org/10.1109/ICHQP61174.2024.10768802 -
Vancouver
Cunha TMOA, Davi MJBB, Oleskovicz M. Enhancing power grid reliability: evaluating machine learning algorithms for fault classification in inverter-based generators interconnection lines [Internet]. Proceedings. 2024 ;[citado 2025 dez. 28 ] Available from: http://dx.doi.org/10.1109/ICHQP61174.2024.10768802 - 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
- Impacts of advances in standards for inverter-based resources controls on distance protection
- Insights and challenges on the protection of grid-forming converter interconnection lines
- Maturity analysis of protection solutions for power systems near inverter-based resources
- Pseudo-incremental normalized quantity-based phase selection method for systems with conventional and inverter-based resources
- An impedance-multi-method-based fault location methodology for transmission lines connected to inverter-based resources
- An improved directional protection element for onshore wind farm collector systems
- Importance of EMT-type simulations for protection studies in power systems with inverter-based resources
- Investigating machine learning capabilities for fault classification and fault region identification in onshore wind farm collectors
Informações sobre o DOI: 10.1109/ICHQP61174.2024.10768802 (Fonte: oaDOI API)
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