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Feasibility of monitoring large wind turbines using photogrammetry

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  • Ozbek, Muammer
  • Rixen, Daniel J.
  • Erne, Oliver
  • Sanow, Gunter

Abstract

Photogrammetry, which is a proven measurement technique based on determination of the 3D coordinates of the points on an object by using two or more images taken from different positions, is proposed to be a promising and cost efficient alternative for monitoring the dynamic behavior of wind turbines. The pros and cons of utilizing this measurement technique for several applications such as dynamic testing or health monitoring of large wind turbines are discussed by presenting the results of the infield tests performed on a 2.5 MW - 80 m diameter - wind turbine.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:12:p:4802-4811
    DOI: 10.1016/j.energy.2010.09.008
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    References listed on IDEAS

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    1. Kong, C. & Bang, J. & Sugiyama, Y., 2005. "Structural investigation of composite wind turbine blade considering various load cases and fatigue life," Energy, Elsevier, vol. 30(11), pages 2101-2114.
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    Cited by:

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    2. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    3. Guo, Jihong & Liu, Chao & Cao, Jinfeng & Jiang, Dongxiang, 2021. "Damage identification of wind turbine blades with deep convolutional neural networks," Renewable Energy, Elsevier, vol. 174(C), pages 122-133.
    4. Song, Zhanfeng & Shi, Tingna & Xia, Changliang & Chen, Wei, 2012. "A novel adaptive control scheme for dynamic performance improvement of DFIG-Based wind turbines," Energy, Elsevier, vol. 38(1), pages 104-117.
    5. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    6. 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.
    7. Shaohui Xiao & Hongjun Liu & Kun Lin, 2023. "Dynamic Performance of Monopile-Supported Wind Turbines (MWTs) under Different Operating and Ground Conditions," Energies, MDPI, vol. 17(1), pages 1-19, December.
    8. Pichan, Mohammad & Rastegar, Hasan & Monfared, Mohammad, 2013. "Two fuzzy-based direct power control strategies for doubly-fed induction generators in wind energy conversion systems," Energy, Elsevier, vol. 51(C), pages 154-162.
    9. Zengyi Zhang & Zhenru Shu, 2024. "Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review," Energies, MDPI, vol. 17(15), pages 1-31, July.
    10. 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.

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