Smart Structures and Systems

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Structural monitoring of wind turbines using wireless sensor networks

  • Swartz, R. Andrew (Department of Civil and Environmental Engineering, Michigan Technological University) ;
  • Lynch, Jerome P. (Department of Civil and Environmental Engineering, University of Michigan) ;
  • Zerbst, Stephan (Institute for Structural Analysis, Leibniz University of Hanover) ;
  • Sweetman, Bert (Department of Maritime Systems Engineering, Texas A&M Galveston) ;
  • Rolfes, Raimund (Institute for Structural Analysis, Leibniz University of Hanover)
  • Received : 2008.05.01
  • Accepted : 2009.07.01
  • Published : 2010.04.25
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Abstract

Monitoring and economical design of alternative energy generators such as wind turbines is becoming increasingly critical; however acquisition of the dynamic output data can be a time-consuming and costly process. In recent years, low-cost wireless sensors have emerged as an enabling technology for structural monitoring applications. In this study, wireless sensor networks are installed in three operational turbines in order to demonstrate their efficacy in this unique operational environment. The objectives of the first installation are to verify that vibrational (acceleration) data can be collected and transmitted within a turbine tower and that it is comparable to data collected using a traditional tethered system. In the second instrumentation, the wireless network includes strain gauges at the base of the structure. Also, data is collected regarding the performance of the wireless communication channels within the tower. In both turbines, collected wireless sensor data is used for off-line, output-only modal analysis of the ambiently (wind) excited turbine towers. The final installation is on a turbine with embedded braking capabilities within the nacelle to generate an "impulse-like" load at the top of the tower. This ability to apply such a load improves the modal analysis results obtained in cases where ambient excitation fails to be sufficiently broad-band or white. The improved loading allows for computation of true mode shapes, a necessary precursor to many conditional monitoring techniques.

Keywords

References

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