Advertisement

Development of temperature-robust damage factor based on sensor fusion for a wind turbine structure

  • Jong-Woong Park
  • Sung-Han Sim
  • Jin-Hak Yi
  • Hyung-Jo Jung
Research Article Special Column on Civil Structure Vibration Based Health and Safety Monitoring

Abstract

Wind power systems have gained much attention due to the relatively high reliability, maturity in technology and cost competitiveness compared to other renewable alternatives. Advances have been made to increase the power efficiency of the wind turbines while less attention has been focused on structural integrity assessment of the structural systems. Vibration-based damage detection has widely been researched to identify damages on a structure based on change in dynamic characteristics. Widely spread methods are natural frequency-based, mode shape-based, and curvature mode shape-based methods. The natural frequency-based methods are convenient but vulnerable to environmental temperature variation which degrades damage detection capability; mode shapes are less influenced by temperature variation and able to locate damage but requires extensive sensor instrumentation which is costly and vulnerable to signal noises. This study proposes novelty of damage factor based on sensor fusion to exclude effect of temperature variation. The combined use of an accelerometer and an inclinometer was considered and damage factor was defined as a change in relationship between those two measurements. The advantages of the proposed method are: 1) requirement of small number of sensor, 2) robustness to change in temperature and signal noise and 3) ability to roughly locate damage. Validation of the proposed method is carried out through numerical simulation on a simplified 5 MW wind turbine model.

Keywords

sensor fusion damage detection structural health monitoring 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hau E. Wind Turbines: Fundamentals, Technologies, Application and Economics. Berlin: Springer, 2006CrossRefGoogle Scholar
  2. 2.
    Askegaard V, Mossing P. Long-term observation of RC-bridge using changes in natural frequencies. Nordic Concrete Research, 1998, 7: 20–27Google Scholar
  3. 3.
    Farrar C R, James G H III. System identification from ambient vibration measurements on a bridge. Journal of Sound and Vibration, 1997, 205(1): 1–18CrossRefGoogle Scholar
  4. 4.
    Swartz R, Lynch J P, Zerbst S, Sweetman B, Rolfes R. Structural monitoring of wind turbines using wireless sensor networks. Smart Structures and Systems, 2010, 6(3): 183–196CrossRefGoogle Scholar
  5. 5.
    Fan W, Qiao P. Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring, 2011, 10(1): 83–111CrossRefGoogle Scholar
  6. 6.
    Park J W, Sim S H, Jung H J. Displacement estimation using multimetric data fusion. IEEE/ASME Transactions on Mechatronics, 2013, 18(6): 1675–1682CrossRefGoogle Scholar
  7. 7.
    Smyth A, Wu M. Multi-rate kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring. Mechanical Systems and Signal Processing, 2007, 21(2): 706–723CrossRefGoogle Scholar
  8. 8.
    Hong Y H, Lee S G, Lee H S. Design of the FEM-FIR filter for displacement reconstruction using accelerations and displacements measured at different sampling rates. Mechanical Systems and Signal Processing, 2013, 38(2): 460–481CrossRefMathSciNetGoogle Scholar
  9. 9.
    Moore E H. On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society, 1920, 26: 394–395Google Scholar
  10. 10.
    Jonkman J, Butterfield S, Musial W, Scott G. Definition of a 5-MW reference wind turbine for offshore system development. Technical Report NREL/TP-500-38060 February, 2009Google Scholar
  11. 11.
    Jonkman J M, Buhl M L. FAST User’s Guide. Technical Report NREL/EL-500-38230 August, 2005Google Scholar
  12. 12.
    Jonkman B.J., and Kilcher L.TurbSim User’s Guide: Version 1.06.00. NREL Technical Report September, 2012Google Scholar
  13. 13.
    Ledbetter H M, Austin M W. Elastic constant versus temperature behavior of three hardened maraging steels. Materials Science and Engineering, 1985, 72(1): 65–69CrossRefGoogle Scholar
  14. 14.
    Juang J N, Pappa R S. An eigensystem realization algorithm for modal parameter identification and model reduction. Journal of Guidance, Control, and Dynamics, 1985, 8(5): 620–627CrossRefzbMATHGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jong-Woong Park
    • 1
  • Sung-Han Sim
    • 2
  • Jin-Hak Yi
    • 3
  • Hyung-Jo Jung
    • 4
  1. 1.Department of Civil and Environmental EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.School of Urban and Environmental EngineeringUlsan National Institute of Science and Technology (UNIST)UlsanRepublic of Korea
  3. 3.Coastal Development & Ocean Engineering Research DivisionKorea Institute of Ocean Science and TechnologyAnsanRepublic of Korea
  4. 4.Department of Civil and Environmental EngineeringKAISTDaejeonRepublic of Korea

Personalised recommendations