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Research Papers

Computationally Efficient Uncertainty Minimization in Wind Turbine Extreme Load Assessments

[+] Author and Article Information
Youngjun Choe

Department of Industrial
and Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: yjchoe@umich.edu

Qiyun Pan

Department of Industrial
and Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: qiyun@umich.edu

Eunshin Byon

Department of Industrial
and Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: ebyon@umich.edu

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received May 21, 2015; final manuscript received April 27, 2016; published online June 14, 2016. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 138(4), 041012 (Jun 14, 2016) (8 pages) Paper No: SOL-15-1151; doi: 10.1115/1.4033511 History: Received May 21, 2015; Revised April 27, 2016

To harvest more energy from wind, wind turbine size has rapidly increased, entailing the serious concern on the reliability of the wind turbine. Accordingly, the international standard requires turbine designers to estimate the extreme load that could be imposed on a turbine during normal operations. At the design stage, physics-based load simulations can be used for this purpose. However, simulating the extreme load associated with a small load exceedance probability is computationally prohibitive. In this study, we propose using importance sampling combined with order statistics to reduce the computational burden significantly while achieving much better estimation accuracy than existing methods.

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Copyright © 2016 by ASME
Topics: Stress
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Figures

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Fig. 1

Overall procedure of SIS1 (Note: the procedure of SIS2 is similar to that of SIS1 with M = NT, Ni=1,i=1,…,NT)

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Fig. 2

Comparison of the original input density, f, with the SIS1 density, qSIS1: (a) in-plane tip deflections and (b) flapwise bending moments

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Fig. 3

Comparison of load exceedance probability of in-plane tip deflections: (a) SIS1 versus MCS and (b) SIS1 versus SIS2

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Fig. 4

Comparison of load exceedance probability of flapwise bending moments: (a) SIS1 versus MCS and (b) SIS1 versus SIS2

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Fig. 6

Comparison of load exceedance probability between SIS1, SIS2, and binning method: (a) in-plane tip deflections and (b) flapwise bending moments

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Fig. 5

Load exceedance probability of in-plane tip deflections (NT=48,000)

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