Correlation Between Wind Turbine Failures and Environmental Conditions: A Machine Learning Approach

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal do ABC (UFABC)-
Autor(es): dc.contributorCemig Generation and Transmission-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorDa Silva, Thadeu Carneiro-
Autor(es): dc.creatorDa Silva Antunes, Fabio Augusto-
Autor(es): dc.creatorTeixeira, Julio Carlos-
Autor(es): dc.creatorContreras, Rodrigo Colnago-
Data de aceite: dc.date.accessioned2025-08-21T15:16:22Z-
Data de disponibilização: dc.date.available2025-08-21T15:16:22Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ACCESS.2025.3551241-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301885-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301885-
Descrição: dc.descriptionWind energy has emerged as a vital renewable source, competing with conventional energy due to its clean and inexhaustible nature. However, the global mass production of wind turbines often disregards the unique environmental conditions of installation sites, leading to performance and reliability challenges. This study applies machine learning methodologies to analyze the correlation between wind turbine failures and local environmental conditions. The research leverages Rough Set Theory to transform instances of undesirable turbine shutdowns - especially those influenced by incomplete tropicalization processes - into actionable decision rules. The findings provide practical insights applicable to wind farms worldwide, enabling optimized maintenance strategies and precise adjustments to protection parameters. These improvements contribute to reducing failure rates, enhancing energy conversion efficiency, and promoting the sustainable expansion of wind energy across diverse geographic and climatic contexts.-
Descrição: dc.descriptionFederal University of ABC (UFABC) Center for Engineering Modeling and Applied Social Sciences-
Descrição: dc.descriptionCemig Generation and Transmission-
Descrição: dc.descriptionInstitute of Biosciences Humanities and Exact Sciences São Paulo State University (UNESP)-
Descrição: dc.descriptionInstitute of Science and Technology Federal University of São Paulo (UNIFESP) Department of Science and Technology-
Descrição: dc.descriptionInstitute of Biosciences Humanities and Exact Sciences São Paulo State University (UNESP)-
Formato: dc.format50043-50058-
Idioma: dc.languageen-
Relação: dc.relationIEEE Access-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectrough sets-
Palavras-chave: dc.subjectwind energy-
Palavras-chave: dc.subjectwind turbine failures-
Palavras-chave: dc.subjectwind turbine projects tropicalization-
Título: dc.titleCorrelation Between Wind Turbine Failures and Environmental Conditions: A Machine Learning Approach-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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