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Modal-based damage identification for the nonlinear model of modern wind turbine blade

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  • Rezaei, Mohammad M.
  • Behzad, Mehdi
  • Moradi, Hamed
  • Haddadpour, Hassan

Abstract

In this paper, the modal-based indices are used in damage identification of the wind turbine blade. In contrast of many of previous researches, the geometric nonlinearity due to the large structural deformation of the modern wind turbines blade is considered. In the first step, the finite element model (FEM) of the rotating blade is solved to obtain the modal features of the deformed structure under operational aerodynamic loading. Next, the accuracy and efficiency of the various modal-based damage indices including the frequency, mode shape, curvature of mode shape, modal assurance, modal strain energy (MSE) and the difference of indices (between the intact and damaged blades) are investigated. To adapt the MSE index calculation in nonlinear modeling, a new approach is introduced to include the effects of the structural nonlinearity. Furthermore, the effect of the damage length, its location and severity and also the effect of rotational speed and amplitude of loading are studied. The generic 5-MW NREL blade is used for the simulation study. The results show enough sensitivity of the mode shape curvature and MSE indices to the local damages. Moreover, the importance of geometric nonlinearity in the damage detection of the modern wind turbines is demonstrated.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:94:y:2016:i:c:p:391-409
    DOI: 10.1016/j.renene.2016.03.074
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    References listed on IDEAS

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    1. Liu, Fushun & Li, Huajun & Li, Wei & Wang, Bin, 2014. "Experimental study of improved modal strain energy method for damage localisation in jacket-type offshore wind turbines," Renewable Energy, Elsevier, vol. 72(C), pages 174-181.
    2. Yang, Wenxian & Little, Christian & Court, Richard, 2014. "S-Transform and its contribution to wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 62(C), pages 137-146.
    3. Rezaei, Mohammad M. & Behzad, Mehdi & Haddadpour, Hassan & Moradi, Hamed, 2015. "Development of a reduced order model for nonlinear analysis of the wind turbine blade dynamics," Renewable Energy, Elsevier, vol. 76(C), pages 264-282.
    4. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
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    Cited by:

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    2. 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.
    3. 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.
    4. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    5. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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