Abstract
The international push in the development of energy that is sustainable in the long term is driving technological improvements in the area of wind-generated energy. Pushing the limits of current knowledge, turbines now feature increasingly slender towers, larger gear boxes, and significantly longer blades in search of greater capacities and improved efficiency. In addition, siting concerns are leading planners to build these structures in increasingly challenging environments where they are subject to harsh and poorly characterized loadings (particularly in off-shore applications where wind and wave interactions are poorly understood). Future safe and economical designs require accurate characterization of design loads, however direct measurement of wind loads on turbines can be problematic due to the disturbance caused by the wind’s interaction with the turbine blades. This paper presents a novel means of estimating wind loading from the dynamic response of the turbine tower to these loads. A model of the structure is derived using the assumed modes method and then updated using dynamically collected acceleration data a there the input-output relationships are established and input loading spectra estimated. The method relies on reduced-order modal space models making it suitable for real-time operation or embedment in a low-cost autonomous (perhaps wireless) monitoring system. Results derived for a full scale structure under lateral seismic loading are presented.
Keywords
- Wireless Sensor Network
- Wind Turbine
- Mode Shape
- Structural Health Monitoring
- Wind Load
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Hernandez, A.V., Swartz, R.A., Zimmerman, A.T. (2011). A Framework for Embedded Load Estimation from Structural Response of Wind Turbines. In: Proulx, T. (eds) Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9428-8_24
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DOI: https://doi.org/10.1007/978-1-4419-9428-8_24
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