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Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines

Author

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  • de Oliveira Nogueira, Tiago
  • Palacio, Gilderlânio Barbosa Alves
  • Braga, Fabrício Damasceno
  • Maia, Pedro Paulo Nunes
  • de Moura, Elineudo Pinho
  • de Andrade, Carla Freitas
  • Rocha, Paulo Alexandre Costa

Abstract

This work innovates by proposing the combination of DFA with the SVM and RBFK methods, two supervised algorithms that use the kernel-method, for the imbalance level classification in a scaled-down wind turbine. The results obtained were compared with other techniques proposed in previous works.

Suggested Citation

  • de Oliveira Nogueira, Tiago & Palacio, Gilderlânio Barbosa Alves & Braga, Fabrício Damasceno & Maia, Pedro Paulo Nunes & de Moura, Elineudo Pinho & de Andrade, Carla Freitas & Rocha, Paulo Alexandre C, 2022. "Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221023124
    DOI: 10.1016/j.energy.2021.122064
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    References listed on IDEAS

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    1. de Moura, Elineudo Pinho & de Abreu Melo Junior, Francisco Erivan & Rocha Damasceno, Filipe Francisco & Campos Figueiredo, Luis Câmara & de Andrade, Carla Freitas & de Almeida, Maurício Soares & Alexa, 2016. "Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals," Renewable Energy, Elsevier, vol. 96(PA), pages 993-1002.
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    Cited by:

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    2. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    3. Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).

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