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Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques

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  • Melo Junior, Francisco Erivan de Abreu
  • de Moura, Elineudo Pinho
  • Costa Rocha, Paulo Alexandre
  • de Andrade, Carla Freitas

Abstract

This work aims to propose a different approach to evaluate the operating conditions of a scaled wind turbine through vibration analysis. The turbine blades were built based on the NREL S809 profile and a 40-cm diameter, while the design blade tip speed ratio (λ) is equal to 7. Masses weighing 0.5, 1.0, and 1.5 g were added to the tip of one or two blades in a varying sequence with the intent of simulating potential problems and producing several scenarios from simple imbalances to severe rotor vibration levels to be compared to the control condition where the three blades and the system were balanced. The signals were processed and classified by a combination of detrended fluctuation analysis with Karhunen-Loève Transform, Gaussian discriminator, and Artificial Neural Network, which are pattern recognition techniques with supervised learning. Good results were achieved by employing the above cited recognition techniques as more than 95% of normal and imbalanced cases were correctly classified. In a general way, it was also possible to identify different levels of blade imbalance, thus proving that the present approach may be an excellent predictive maintenance tool for vibration monitoring of wind turbines.

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  • 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.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:556-565
    DOI: 10.1016/j.energy.2019.01.042
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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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    3. Moura Carneiro, F.O. & Barbosa Rocha, H.H. & Costa Rocha, P.A., 2013. "Investigation of possible societal risk associated with wind power generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 30-36.
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    5. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
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    Cited by:

    1. 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).

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