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In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation

Author

Listed:
  • Weiwu Feng

    (College of Civil Engineering and Architecture, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Da Yang

    (Sany Heavy Industry Co., Ltd., Changsha 410100, China)

  • Wenxue Du

    (College of Civil Engineering and Architecture, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Qiang Li

    (College of Civil Engineering and Architecture, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

Abstract

Structural health monitoring (SHM) and the operational condition assessment of blades are greatly important for the operation of wind turbines that are at a high risk of disease in service for more than 5 years. Since certain types of blade faults only occur during wind turbine operation, it is more significant to perform in situ SHM of rotating full-scale blades than existing SHM of small-scale blades or static testing of full-scale blades. Considering that these blades are usually not prefabricated with relevant sensors, this study performed SHM and condition assessment of full-scale blades in operation with stereo digital image correlation. A self-calibration method adapted to the outdoors with a large field of view was introduced based on the speckled patterns. To accurately obtain the in- and off-plane deformation, a new reference frame is constructed at the center of the rotation of the blades. The 3D displacements of the points of interest (POIs) on the blade of a 2 MW wind turbine were characterized. Furthermore, the frequency spectrum of the measured 3D displacements of the blades was compared with the blades with the faults. The results showed that the introduced technique is a convenient and nondestructive technique that enables SHM of full-scale wind turbine blades in operation.

Suggested Citation

  • Weiwu Feng & Da Yang & Wenxue Du & Qiang Li, 2023. "In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13783-:d:1240811
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    References listed on IDEAS

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    1. Sikandar Khan, 2023. "A Modeling Study Focused on Improving the Aerodynamic Performance of a Small Horizontal Axis Wind Turbine," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    2. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    3. 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.
    4. Bingchuan Sun & Hongmei Cui & Zhongyang Li & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang, 2022. "Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    5. Wang, Zixuan & Qin, Bo & Sun, Haiyue & Zhang, Jian & Butala, Mark D. & Demartino, Cristoforo & Peng, Peng & Wang, Hongwei, 2023. "An imbalanced semi-supervised wind turbine blade icing detection method based on contrastive learning," Renewable Energy, Elsevier, vol. 212(C), pages 251-262.
    6. Zheng Liu & Xin Liu & Kan Wang & Zhongwei Liang & José A.F.O. Correia & Abílio M.P. De Jesus, 2019. "GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades," Energies, MDPI, vol. 12(6), pages 1-15, March.
    7. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    8. Chandrasekhar, Kartik & Stevanovic, Nevena & Cross, Elizabeth J. & Dervilis, Nikolaos & Worden, Keith, 2021. "Damage detection in operational wind turbine blades using a new approach based on machine learning," Renewable Energy, Elsevier, vol. 168(C), pages 1249-1264.
    9. Luo, Kai & Chen, Liang & Liang, Wei, 2022. "Structural health monitoring of carbon fiber reinforced polymer composite laminates for offshore wind turbine blades based on dual maximum correlation coefficient method," Renewable Energy, Elsevier, vol. 201(P1), pages 1163-1175.
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