Structural Monitoring and Maintenance

  • 제4권2호
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  • Pages.115-131
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  • 2017
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Monitoring of wind turbine blades for flutter instability

  • Chen, Bei (Key Laboratory for Wind and Bridge Engineering, Department of Civil Engineering, Hunan University) ;
  • Hua, Xu G. (Key Laboratory for Wind and Bridge Engineering, Department of Civil Engineering, Hunan University) ;
  • Zhang, Zi L. (Department of Engineering, Aarhus University) ;
  • Basu, Biswajit (School of Engineering, Trinity College) ;
  • Nielsen, Soren R.K. (Department of Civil Engineering, Aalborg University)
  • 투고 : 2017.02.16
  • 심사 : 2017.06.08
  • 발행 : 2017.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Classical flutter of wind turbine blades indicates a type of aeroelastic instability with fully attached boundary layer where a torsional blade mode couples to a flapwise bending mode, resulting in a mutual rapid growth of the amplitudes. In this paper the monitoring problem of onset of flutter is investigated from a detection point of view. The criterion is stated in terms of the exceeding of a defined envelope process of a specific maximum torsional vibration threshold. At a certain instant of time, a limited part of the previously measured torsional vibration signal at the tip of blade is decomposed through the Empirical Mode Decomposition (EMD) method, and the 1st Intrinsic Mode Function (IMF) is assumed to represent the response in the flutter mode. Next, an envelope time series of the indicated modal response is obtained in terms of a Hilbert transform. Finally, a flutter onset criterion is proposed, based on the indicated envelope process. The proposed online flutter monitoring method provided a practical and direct way to detect onset of flutter during operation. The algorithm has been illustrated by a 907-DOFs aeroelastic model for wind turbines, where the tower and the drive train is modelled by 7 DOFs, and each blade by means of 50 3-D Bernoulli-Euler beam elements.

키워드

과제정보

연구 과제 주관 기관 : National Science Foundation of China, European Commission

참고문헌

  1. Ako, H. (1992), Flutter control system for aircraft wings, US Patent 5,135,186, Google Patents, USA.
  2. Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L. C., Natarajan, A. and Hansen, M. (2013), Description of the DTU 10 MW Reference Wind Turbine 2013, DTU Wind Energy Report-I-0092, Cph, Denmark.
  3. Bir, G. and Jonkman, J. (2007), "Aeroelastic instabilities of large offshore and onshore wind turbines- art. no. 012069", Science of Making Torque from Wind. Journal of Physics Conference Series, 75, Denmark, Lyngby, August.
  4. Chen, B., Zhang, Z.L., Hua, X.G., Nielsen, S.R.K. and Basu, B. (2016), "Enhancement of flutter stability in wind turbines with a new type of passive damper of torsional rotation of blades", J. Wind Eng. Ind. Aerod., under review.
  5. Chu, P.C., Fan, C.W. and Huang, N. (2014), "Derivative-optimized empirical mode decomposition for the Hilbert-Huang transform", J. Comput. Appl. Math., 259, 57-64. https://doi.org/10.1016/j.cam.2013.03.046
  6. Do, D.P.N., Lee, Y. and Choi, J. (2016), "Hourly average wind speed simulation and forecast based on ARMA model in Jeju Island, Korea", J. Elec. Eng. Technol., 11(6), 1548-1555. https://doi.org/10.5370/JEET.2016.11.6.1548
  7. Hansen, M.H. (2004), "Aeroelastic stability analysis for for wind turbines using an eigen value approach", Wind Energy, 7(2), 133-143 https://doi.org/10.1002/we.116
  8. Hansen, M.H. (2007), "Aeroelastic instability for wind turbines", Wind Energy, 10(6), 551-577. https://doi.org/10.1002/we.242
  9. Hansen, M.O.L. (2015), Aerodynamics of Wind Turbines, (3nd Ed.), Routledge, UK.
  10. Hayat, K., De Lecea, A.G.M., Moriones, C.D. and Ha, S.K. (2016), "Flutter performance of bend- twist coupled large-scale wind turbine blades", J. Sound Vib., 370, 149-162. https://doi.org/10.1016/j.jsv.2016.01.032
  11. Hsu, M.C., Akkerman, I. and Bazilevs, Y. (2014), "Finite element simulation of wind turbine aerodynamics: validation study using NREL Phase VI experiment", Wind Energy, 17(3), 461-481. https://doi.org/10.1002/we.1599
  12. Huang, N.E. (2014), Hilbert-Huang transform and its applications, World Scientific, Singapore.
  13. Huang, N.E. and Wu, Z.H. (2008), "A review on Hilbert-Huang transform: Method and its applications to geophysical studies", Rev. Geophysics, 46(2).
  14. Johnson, W. (1980), Helicopter Theory, Princeton Univ. Press, USA.
  15. Krenk, S. (2011), "Explicit calibration and simulation of stochastic fields by low-order ARMA processes", Eccomas Thematic Conference-Compdyn 2011.
  16. Laks, J., Pao, L., Wright, A., Kelley, N, and Jonkman, B.(2011), "The use of preview wind measurements for blade pitch control", Mechatronics, 21(4), 668-681. https://doi.org/10.1016/j.mechatronics.2011.02.003
  17. Lobitz, D. W. and Veer, P. S. (1998), "Aeroelastic behavior of twist-coupled HAWT blades", AIAA Paper, 98-0029.
  18. Lobitz, D.W. (2004), "Aeroelastic stability predictions for a MW-sized blade", Wind Energy, 7(3), 211-224. https://doi.org/10.1002/we.120
  19. Luo, Y. and Chen, Y.Q. (2012), "Stabilizing and robust fractional order PI controller synthesis for first order plus time delay systems", Automatica,48(9), 2159-2167. https://doi.org/10.1016/j.automatica.2012.05.072
  20. Lurie, A. I. (2013), Analytical mechanics, Springer Science & Business Media Press, New York, USA.
  21. Mevel, L., Basseville, M. and Benveniste, A. (2005), "Fast in-flight detection of flutter onset: a statistical approach", J. Guid. Control, Dynam., 28(3), 431-438. https://doi.org/10.2514/1.6184
  22. Nayfeh, A.H. and Mook, D.T. (1995), Nonlinear Oscillations, John Wiley and Sons, New York, USA.
  23. Ogata, K. (2010), Modern Control Engineering, (5th Edition), Pearson, USA.
  24. Politakis, G., Haans, W. and Van Bussel, G. J. W. (2008), "Suppression of classical flutter using a 'smart blade", Proceedings of the 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, USA, January.
  25. Popelka, D. (1982), Aeroelastic stability analysis of a Darrieus wind turbine, Sandia National Laboratories, Albuquerque, California, USA.
  26. Tang, J.P., Chiou, D.J., Chen, C.W., Chiang, W.L., Hsu, W.K., Chen, C.Y. and Liu, T.Y. (2011), "RETRACTED: A case study of damage detection in benchmark buildings using a Hilbert-Huang Transform-based method", J. Vib. Control, 17(4), 623-636. https://doi.org/10.1177/1077546309360053
  27. Tcherniak, D. (2011), "Rotor anisotropy as a blade damage indicator for wind turbine structural health monitoring systems", Mech. Syst. Signal Pr., 74, 183-198.
  28. Vatne, S.R. (2011), "Aeroelastic instability and flutter for a 10 MW wind turbine", Institutt for energi-og prosessteknikk.
  29. Yan, J. and Lu, L. (2014), "Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis", Signal Processing, 98, 74-87. https://doi.org/10.1016/j.sigpro.2013.11.012
  30. Zhang, P. and Huang, S. (2011), "Review of aeroelasticity for wind turbine: Current status, research focus and future perspectives", Frontiers in Energy, 5(4), 419-434.
  31. Zhang, Z.L., Nielsen, S.R.K., Blaabjerg, F. and Zhou, D. (2014), "Dynamics and control of lateral tower vibrations in offshore wind turbines by means of active generator torque", Energies, 7(11), 7746-7772. https://doi.org/10.3390/en7117746
  32. Zhou, W. S., Maalej, S., Zouari, R., Mevel, L., De B. and IRISA-Campus (2007), "Flutter monitoring with the statistical subspace method for the 2-d structure", Proceedings of the 25th International Modal Analysis Conference (IMAC-XXV), Orlando, Fl, US.
  33. Zouari, R., Mevel, L. and Basseville, M. (2012), "Adaptive statistical approach to flutter detection", J. Aircraft, 49(3), 735-748. https://doi.org/10.2514/1.C000260

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