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Proceedings Paper

Probabilistic analysis of mean-response along-wind induced vibrations on wind turbine towers using wireless network data sensors
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Paper Abstract

Wind turbine systems are attracting considerable attention due to concerns regarding global energy consumption as well as sustainability. Advances in wind turbine technology promote the tendency to improve efficiency in the structure that support and produce this renewable power source, tending toward more slender and larger towers, larger gear boxes, and larger, lighter blades. The structural design optimization process must account for uncertainties and nonlinear effects (such as wind-induced vibrations, unmeasured disturbances, and material and geometric variabilities). In this study, a probabilistic monitoring approach is developed that measures the response of the turbine tower to stochastic loading, estimates peak demand, and structural resistance (in terms of serviceability). The proposed monitoring system can provide a real-time estimate of the probability of exceedance of design serviceability conditions based on data collected in-situ. Special attention is paid to wind and aerodynamic characteristics that are intrinsically present (although sometimes neglected in health monitoring analysis) and derived from observations or experiments. In particular, little attention has been devoted to buffeting, usually non-catastrophic but directly impacting the serviceability of the operating wind turbine. As a result, modal-based analysis methods for the study and derivation of flutter instability, and buffeting response, have been successfully applied to the assessment of the susceptibility of high-rise slender structures, including wind turbine towers. A detailed finite element model has been developed to generate data (calibrated to published experimental and analytical results). Risk assessment is performed for the effects of along wind forces in a framework of quantitative risk analysis. Both structural resistance and wind load demands were considered probabilistic with the latter assessed by dynamic analyses.

Paper Details

Date Published: 31 March 2011
PDF: 15 pages
Proc. SPIE 7984, Health Monitoring of Structural and Biological Systems 2011, 798421 (31 March 2011); doi: 10.1117/12.881131
Show Author Affiliations
Antonio Velazquez, Michigan Technological Univ. (United States)
Raymond A. Swartz, Michigan Technological Univ. (United States)


Published in SPIE Proceedings Vol. 7984:
Health Monitoring of Structural and Biological Systems 2011
Tribikram Kundu, Editor(s)

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