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Application of videometric technique to deformation measurement for large-scale composite wind turbine blade

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  • Yang, Jinshui
  • Peng, Chaoyi
  • Xiao, Jiayu
  • Zeng, Jingcheng
  • Yuan, Yun

Abstract

The size of wind turbine blades is expected to increase considerably in the future. Since the modern wind turbine blade is generally large-scale, faulty design production results in high expense. To achieve a desired wind turbine blade, a better understanding of the structural behavior on different scale is necessary. Although the structural deformation of the full-scale blade under various loads is an important parameter in the analysis of structural behavior and the blade’s performance, there are many difficulties in an accurate deformation measurement of the large-scale wind turbine blade. A videometric technique reported here was developed to determine the large-scale blade deformations. The technique principle and methodology will be presented in the current paper, which involves the detail of implementation in the large-scale blade deformation measurement. Some application examples with the obtained data and their analysis are presented, which demonstrates the technique’s application on large-scale wind turbine blade, and which is relevant to understanding the structural behavior of wind turbine blade in the full-scale tests and during operation. The videometric technique will be useful for structural investigation of composite wind turbine blades. A wide variety of other applications, especially those involving fieldwork, could exploit this implementation of videometric.

Suggested Citation

  • Yang, Jinshui & Peng, Chaoyi & Xiao, Jiayu & Zeng, Jingcheng & Yuan, Yun, 2012. "Application of videometric technique to deformation measurement for large-scale composite wind turbine blade," Applied Energy, Elsevier, vol. 98(C), pages 292-300.
    • Handle: RePEc:eee:appene:v:98:y:2012:i:c:p:292-300
    DOI: 10.1016/j.apenergy.2012.03.040
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    References listed on IDEAS

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    Cited by:

    1. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    2. Zhou, H.F. & Zheng, J.F. & Xie, Z.L. & Lu, L.J. & Ni, Y.Q. & Ko, J.M., 2017. "Temperature effects on vision measurement system in long-term continuous monitoring of displacement," Renewable Energy, Elsevier, vol. 114(PB), pages 968-983.
    3. Li, Jimeng & Chen, Xuefeng & Du, Zhaohui & Fang, Zuowei & He, Zhengjia, 2013. "A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis," Renewable Energy, Elsevier, vol. 60(C), pages 7-19.
    4. Mathijs Peeters & Gilberto Santo & Joris Degroote & Wim Van Paepegem, 2018. "Comparison of Shell and Solid Finite Element Models for the Static Certification Tests of a 43 m Wind Turbine Blade," Energies, MDPI, vol. 11(6), pages 1-18, May.
    5. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    6. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    7. 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.
    8. Xiaowen Song & Zhitai Xing & Yan Jia & Xiaojuan Song & Chang Cai & Yinan Zhang & Zekun Wang & Jicai Guo & Qingan Li, 2022. "Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage," Energies, MDPI, vol. 15(20), pages 1-17, October.

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