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Vertical Axis Wind Turbine Operational Modal Analysis in Sheared Wind Flow

Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

There has been increased interest in implementing co-located renewable energy generation systems in urban areas, and vertical axis wind turbines (VAWTs) are a candidate technology for capturing the unsteady, omnidirectional winds that are typical of an urban environment. Before the technology can be widely deployed, the issues of reliability and environmental noise must be addressed. Knowledge of the structural dynamic response of the rotor blades as a function of the wind state that enters the rotor will facilitate a better understanding of both reliability (of the blades and driveline bearings) and noise (frequency content of the blade motions that causes structure-borne noise). An experimental modal model was developed to relate the forces introduced by wind loads to the response of the structure. Experimental modal analysis was performed on a 600 W lift-type (Darrieus) VAWT for a range of azimuth angles and tower heights. Additionally, operational modal analysis was conducted in an indoor simulated wind environment test bed. A modal filtering technique was applied and verified using an electro-dynamic shaker and was then used to analyze the operational data. The effects of wind speed and shear on the modal response were investigated. The key results were a shift down in resonance frequencies with increasing height of the turbine tower for rotor modes coupled to the tower response and significant changes in operational modal response for non-uniform (sheared) wind conditions, especially from the 5 Hz mode of vibration. Future work will involve operational testing of vibration and acoustic emissions of the VAWT on a building rooftop.

Keywords

Wind Turbine Response Spectrum Wind Shear Modal Filter Modal Assurance Criterion 
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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Copyright information

© Springer Science+Buisness Media, LLC 2012

Authors and Affiliations

  1. 1.Center for Systems Integrity, School of Mechanical EngineeringPurdue UniversityWest LafayetteUSA

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