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Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines

Author

Listed:
  • Feng Gao

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiaojiang Wu

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Qiang Liu

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Juncheng Liu

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiyun Yang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Damaged wind turbine (WT) blades have an imbalanced load and abnormal vibration, which affects their safe and stable operation or even results in blade rupture. To solve this problem, this study proposes a new method to detect damage in WT blades using wavelet packet energy spectrum analysis and operational modal analysis. First, a wavelet packet transform is used to analyze the tip displacement of the blades to obtain the energy spectrum. The damage is detected preliminarily based on the energy change in different frequency bands. Subsequently, an operational modal analysis method is used to obtain the modal parameters of the blade sections and the damage is located based on the modal strain energy change ratio (MSECR). Finally, the professional WT simulation software GH (Garrad Hassan) Bladed is used to simulate the blade damage and the results are verified by developing an online fault diagnosis platform integrated with MATLAB. The results show that the proposed method is able to diagnose and locate the damage accurately and provide a basis for further research of online damage diagnosis for WT blades.

Suggested Citation

  • Feng Gao & Xiaojiang Wu & Qiang Liu & Juncheng Liu & Xiyun Yang, 2019. "Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines," Energies, MDPI, vol. 12(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:522-:d:204048
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    References listed on IDEAS

    as
    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    2. 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.
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    4. Rezaei, Mohammad M. & Behzad, Mehdi & Moradi, Hamed & Haddadpour, Hassan, 2016. "Modal-based damage identification for the nonlinear model of modern wind turbine blade," Renewable Energy, Elsevier, vol. 94(C), pages 391-409.
    5. Tang, Jialin & Soua, Slim & Mares, Cristinel & Gan, Tat-Hean, 2016. "An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades," Renewable Energy, Elsevier, vol. 99(C), pages 170-179.
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    Cited by:

    1. Yuri Vankov & Aleksey Rumyantsev & Shamil Ziganshin & Tatyana Politova & Rinat Minyazev & Ayrat Zagretdinov, 2020. "Assessment of the Condition of Pipelines Using Convolutional Neural Networks," Energies, MDPI, vol. 13(3), pages 1-12, February.
    2. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.

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