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Estimation of fatigue and extreme load distributions from limited data with application to wind energy systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimation of fatigue and extreme load distributions from limited data with application to wind energy systems.
Author:	Fitzwater, LeRoy Marvin.
Description:	348 p.
Notes:	Adviser: C. Allin Cornell.
Notes:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4505.
Contained By:	Dissertation Abstracts International64-09B.
Subject:	Engineering, Civil.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3104219
ISBN:	0496517872

Estimation of fatigue and extreme load distributions from limited data with application to wind energy systems.
Fitzwater, LeRoy Marvin.

Estimation of fatigue and extreme load distributions from limited data with application to wind energy systems. [electronic resource] - 348 p.

Adviser: C. Allin Cornell.

Thesis (Ph.D.)--Stanford University, 2003.

An estimate of the distribution of fatigue ranges or extreme loads for wind turbines may be obtained by separating the problem into two uncoupled parts, (1) a turbine specific portion, independent of the site and (2) a site-specific description of environmental variables. With this approach, we can address the probabilistic description of each portion independently. First, we consider the appropriate choice of probabilistic model for describing the turbine specific response. For extremes we consider several models over a range of different time scales e.g., process, local peak, and global peak models, and for modeling fatigue ranges we consider higher-moment probability models, which more directly reflect fatigue damage. Second, the site-specific portion is described by a joint probability distribution of a vector of environmental variables, which characterize the wind process at the hub-height of the wind turbine. Finally, the two portions are combined by numerical integration to obtain an estimate of the long-term marginal distribution of extreme loads or fatigue ranges. We assess the efficacy of these models to obtain accurate estimates, including various levels of epistemic uncertainty, of the turbine response.

ISBN: 0496517872Subjects--Topical Terms:

212394
Engineering, Civil.

Estimation of fatigue and extreme load distributions from limited data with application to wind energy systems.
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502 $a Thesis (Ph.D.)--Stanford University, 2003.
520 # $a An estimate of the distribution of fatigue ranges or extreme loads for wind turbines may be obtained by separating the problem into two uncoupled parts, (1) a turbine specific portion, independent of the site and (2) a site-specific description of environmental variables. With this approach, we can address the probabilistic description of each portion independently. First, we consider the appropriate choice of probabilistic model for describing the turbine specific response. For extremes we consider several models over a range of different time scales e.g., process, local peak, and global peak models, and for modeling fatigue ranges we consider higher-moment probability models, which more directly reflect fatigue damage. Second, the site-specific portion is described by a joint probability distribution of a vector of environmental variables, which characterize the wind process at the hub-height of the wind turbine. Finally, the two portions are combined by numerical integration to obtain an estimate of the long-term marginal distribution of extreme loads or fatigue ranges. We assess the efficacy of these models to obtain accurate estimates, including various levels of epistemic uncertainty, of the turbine response.
520 # $a The national and international structural design standards by which wind turbines are certified greatly impact the competitive edge of the companies participating in the wind energy industry. By developing probabilistic methods to estimate both fatigue and extreme load distributions, we can reduce the cost of energy while improving safety and reliability.
520 # $a The numerical integration described above can be computationally intensive. Two alternative approaches are presented for obtaining accurate estimates of extreme loads. (1) The number of levels of integration required are reduced by considering those random variables that contribute the least to the variability in the estimate of the marginal distribution of the extreme loads deterministic at fractile levels higher than the mean. (2) A first-order reliability method is used to obtained contours of the environmental variables for a prescribed reliability level. An estimate of the extreme turbine response is obtained by searching along the contour for the maximum turbine response. We find that reasonable estimates are obtained from these approximate methods compared with the numerical integration approach.
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