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Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network

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  • Xing, Zuoxia
  • Chen, Mingyang
  • Cui, Jia
  • Chen, Zhe
  • Xu, Jian

Abstract

Rotor imbalances present a serious problem for wind turbines. In particular, for offshore wind turbines, aerodynamic imbalance can have a severe impact because of the large rotor size. In this study, the impact of the aerodynamic imbalance is investigated. A novel framework for detecting aerodynamic imbalance is proposed. Firstly, a model of a 3MW direct-driven wind turbine was developed. The signals were acquired to test and verify the impact of aerodynamic imbalance. Secondly, a method based on optimized maximum correlated kurtosis deconvolution was proposed for the primary detection. The intrinsic mode functions of nacelle vibration were adopted as the input variable. The weak unbalanced signals could be discerned. Moreover, the azimuth of rotor allows the unbalanced blades to be obtained. Thirdly, a convolutional neural network with a new structure was used to determine the magnitudes of aerodynamic imbalances. The first layer of the convolutional neural network is sufficiently wide for improving feature extraction, it could make nacelle acceleration as the input. This structure exhibits accuracy and robustness satisfactorily. Finally, the framework was demonstrated in a high-fidelity simulation environment. Different scenarios of aerodynamic imbalance were tested, the results demonstrate the satisfactory performance of the proposed framework.

Suggested Citation

  • Xing, Zuoxia & Chen, Mingyang & Cui, Jia & Chen, Zhe & Xu, Jian, 2022. "Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network," Renewable Energy, Elsevier, vol. 197(C), pages 1020-1033.
  • Handle: RePEc:eee:renene:v:197:y:2022:i:c:p:1020-1033
    DOI: 10.1016/j.renene.2022.07.152
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    References listed on IDEAS

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    1. Hübner, G.R. & Pinheiro, H. & de Souza, C.E. & Franchi, C.M. & da Rosa, L.D. & Dias, J.P., 2021. "Detection of mass imbalance in the rotor of wind turbines using Support Vector Machine," Renewable Energy, Elsevier, vol. 170(C), pages 49-59.
    2. Francesco Castellani & Abdelgalil Eltayesh & Matteo Becchetti & Antonio Segalini, 2021. "Aerodynamic Analysis of a Wind-Turbine Rotor Affected by Pitch Unbalance," Energies, MDPI, vol. 14(3), pages 1-16, January.
    3. Hur, S. & Recalde-Camacho, L. & Leithead, W.E., 2017. "Detection and compensation of anomalous conditions in a wind turbine," Energy, Elsevier, vol. 124(C), pages 74-86.
    4. de Novaes Pires Leite, Gustavo & da Cunha, Guilherme Tenório Maciel & dos Santos Junior, José Guilhermino & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko & Ros, 2021. "Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1183-1194.
    5. Jenny Niebsch & Ronny Ramlau & Thien T. Nguyen, 2010. "Mass and Aerodynamic Imbalance Estimates of Wind Turbines," Energies, MDPI, vol. 3(4), pages 1-15, April.
    6. Abraham, Aliza & Hong, Jiarong, 2021. "Operational-dependent wind turbine wake impact on surface momentum flux," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
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