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Temperature effects on vision measurement system in long-term continuous monitoring of displacement

Author

Listed:
  • Zhou, H.F.
  • Zheng, J.F.
  • Xie, Z.L.
  • Lu, L.J.
  • Ni, Y.Q.
  • Ko, J.M.

Abstract

Videogrammetry has been recognized as a promising method for monitoring the dynamics of wind turbines. On the way forward, the environmental effects on the measurement accuracy of the videogrammetry are key issues should be solved. This study therefore carried out an investigation into the temperature-induced measurement error of the videometric technique in long-term continuous monitoring of displacement. First, videometric measurement tests specifically targeted for improving the applicability of the videogrammetry to wind turbine blade monitoring were conducted. Making use of the measurement data, the characteristics of the temperature-induced measurement errors were obtained by examining them as a whole as well as their wavelet decomposed components. Comprehensive correlation analyses were performed to quantify the degree of correlation between measurement error and temperature change. It is found that the vertical displacement measurement error and temperature change have a satisfactory linear relationship, while the relationship between horizontal displacement measurement error and temperature change is more scattered. Making use of the unique features of the deformation and thermal expansion of a utility-scale wind turbine blade, it is likely to subtract the temperature-induced measurement error from the measured displacement with the help of the relationship between measurement error and temperature change or a time-frequency approach.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:114:y:2017:i:pb:p:968-983
    DOI: 10.1016/j.renene.2017.07.104
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    References listed on IDEAS

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    1. Schubel, P.J. & Crossley, R.J. & Boateng, E.K.G. & Hutchinson, J.R., 2013. "Review of structural health and cure monitoring techniques for large wind turbine blades," Renewable Energy, Elsevier, vol. 51(C), pages 113-123.
    2. Ozbek, Muammer & Rixen, Daniel J. & Erne, Oliver & Sanow, Gunter, 2010. "Feasibility of monitoring large wind turbines using photogrammetry," Energy, Elsevier, vol. 35(12), pages 4802-4811.
    3. 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.
    4. Hameed, Z. & Ahn, S.H. & Cho, Y.M., 2010. "Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation," Renewable Energy, Elsevier, vol. 35(5), pages 879-894.
    5. Esu, O.O. & Lloyd, S.D. & Flint, J.A. & Watson, S.J., 2016. "Feasibility of a fully autonomous wireless monitoring system for a wind turbine blade," Renewable Energy, Elsevier, vol. 97(C), pages 89-96.
    6. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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