Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads
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Published:2019-09-10
Issue:3
Volume:4
Page:479-513
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ISSN:2366-7451
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Container-title:Wind Energy Science
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language:en
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Short-container-title:Wind Energ. Sci.
Author:
Robertson Amy N.,Shaler Kelsey,Sethuraman Latha,Jonkman Jason
Abstract
Abstract. Proper wind turbine design relies on the ability to accurately
predict ultimate and fatigue loads of turbines. The load analysis
process requires precise knowledge of the expected wind-inflow conditions as well as
turbine structural and aerodynamic properties. However, uncertainty in most
parameters is inevitable. It is therefore important to understand the impact
such uncertainties have on the resulting loads. The goal of this work is to
assess which input parameters have the greatest influence on turbine power,
fatigue loads, and ultimate loads during normal turbine operation. An
elementary effects sensitivity analysis is performed to identify the most
sensitive parameters. Separate case studies are performed on (1) wind-inflow
conditions and (2) turbine structural and aerodynamic properties, both
cases using the National Renewable Energy Laboratory 5 MW baseline wind
turbine. The Veers model was used to generate synthetic International
Electrotechnical Commission (IEC) Kaimal turbulence spectrum inflow. The focus is
on individual parameter sensitivity, though interactions between parameters
are considered. The results of this work show that for wind-inflow conditions, turbulence in the primary wind direction and shear are the most sensitive parameters for turbine loads, which is expected. Secondary parameters of importance are identified as veer, u-direction integral length, and u components of the IEC coherence model, as well as the exponent. For the turbine properties, the most sensitive parameters are yaw misalignment and outboard lift coefficient distribution; secondary parameters of importance are inboard lift distribution, blade-twist distribution, and blade mass imbalance. This
information can be used to help establish uncertainty bars around the predictions
of engineering models during validation efforts, and provide insight to
probabilistic design methods and site-suitability analyses.
Funder
National Renewable Energy Laboratory
Publisher
Copernicus GmbH
Subject
Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
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