Publicação

Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods

Detalhes bibliográficos
Resumo:	The last few years have been marked by the insertion of renewable technologies in the global energy matrix, such as wind and solar energy, which are considered clean energies with low environmental impact. Wind turbines, responsible for the energy conversion process, are complex equipment that are expensive and susceptible to numerous failures. Monitoring turbine components can help detect failures before they occur, reducing equipment maintenance costs. This work compares the training time of different techniques for tuning hyperparameters in supervised machine-learning models for fault detection in wind turbines. Results show the importance of data optimization during model training.
Autores principais:	Pinna, Danielle
Outros Autores:	Toso, Rodrigo; Semaan, Gustavo; Sá, Fernando de; Pereira, Ana I.; Ferreira, Ângela P.; Soares, Jorge; Brandão, Diego
Assunto:	Wind turbine Machine learning Fault classification
Ano:	2024
País:	Portugal
Tipo de documento:	comunicação em conferência
Tipo de acesso:	acesso restrito
Instituição associada:	Universidade Politécnica de Bragança
Idioma:	inglês
Origem:	Biblioteca Digital da UPB

Descrição
Resumo:	The last few years have been marked by the insertion of renewable technologies in the global energy matrix, such as wind and solar energy, which are considered clean energies with low environmental impact. Wind turbines, responsible for the energy conversion process, are complex equipment that are expensive and susceptible to numerous failures. Monitoring turbine components can help detect failures before they occur, reducing equipment maintenance costs. This work compares the training time of different techniques for tuning hyperparameters in supervised machine-learning models for fault detection in wind turbines. Results show the importance of data optimization during model training.

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