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Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals

Author

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  • 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
  • Alexandre Costa Rocha, Paulo

Abstract

This work proposes to identify different imbalance levels in a scaled wind turbine through vibration signals analysis. The experiment was designed in such a way that the acquired signals could be classified in different ways. A combination of detrended fluctuation analysis of acquired signals and different classifiers, supervised and unsupervised, was performed. The optimum number of groups suggested by k-means clustering, an automatic classifier with unsupervised learning algorithm, differs from the number of classes (or subsets) defined during the experimental planning, presenting another approach to the possible classification of vibration signals. Additionally, three supervised learning algorithms (namely neural networks, Gaussian classifier and Karhunen-Loève transform) were employed to this end, classifying the collected data in some predefined amounts of classes. The results obtained for the test data, just a little different regarding the training data, also confirmed their capability to identify new signals. The results presented are promising, giving important contributions to the development of an automatic system for imbalance diagnosis in wind turbines.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:993-1002
    DOI: 10.1016/j.renene.2016.05.005
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    References listed on IDEAS

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    1. Jennings, Heather D & Ivanov, Plamen Ch & M. Martins, Allan de & da Silva, P.C & Viswanathan, G.M, 2004. "Variance fluctuations in nonstationary time series: a comparative study of music genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 585-594.
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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).
    2. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    3. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    4. 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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