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

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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.
    2. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    3. P. M. Lerman, 1980. "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 77-84, March.
    4. Yang, Bin & Sun, Dongbai, 2013. "Testing, inspecting and monitoring technologies for wind turbine blades: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 515-526.
    5. Francisco J. Solis & Roger J.-B. Wets, 1981. "Minimization by Random Search Techniques," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 19-30, February.
    6. Wang, Jinjiang & Liang, Yuanyuan & Zheng, Yinghao & Gao, Robert X. & Zhang, Fengli, 2020. "An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples," Renewable Energy, Elsevier, vol. 145(C), pages 642-650.
    7. Sagol, Ece & Reggio, Marcelo & Ilinca, Adrian, 2013. "Issues concerning roughness on wind turbine blades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 514-525.
    8. Dalili, N. & Edrisy, A. & Carriveau, R., 2009. "A review of surface engineering issues critical to wind turbine performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 428-438, February.
    9. Han, Woobeom & Kim, Jonghwa & Kim, Bumsuk, 2018. "Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 817-823.
    10. Melo Junior, Francisco Erivan de Abreu & de Moura, Elineudo Pinho & Costa Rocha, Paulo Alexandre & de Andrade, Carla Freitas, 2019. "Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques," Energy, Elsevier, vol. 171(C), pages 556-565.
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    Cited by:

    1. Rubio, José de Jesús & Garcia, Donaldo & Sossa, Humberto & Garcia, Ivan & Zacarias, Alejandro & Mujica-Vargas, Dante, 2023. "Energy processes prediction by a convolutional radial basis function network," Energy, Elsevier, vol. 284(C).
    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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