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Title: Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation

Abstract

Estimating long return period extreme wind turbine loads is made especially difficult by the large response variability for 'the same' environmental conditions. To alleviate this, we have 'opened up the black box' of the turbulent wind generation stage of the simulations. Exploiting the notion of 'temporal coherence' allows us to manipulate the turbulent inflow to target extreme wind conditions, while at the same time quantifying 'how probable these are.' The resulting importance sampling load estimates achieve a significantly lower exceedance probability (i.e., they represent much longer return periods) than estimates using the same number of samples (i.e., the same computational resources) but only a standard Monte Carlo estimate.

Authors:
 [1];  [1];  [1];  [1];  [1]; ORCiD logo [1];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. DTU Wind Energy
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1547232
Report Number(s):
NREL/CP-2C00-72692
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the AIAA SciTech 2019 Forum, 7-11 January 2019, San Diego, California
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; temporal coherence; sampling; wind turbine; extreme loads

Citation Formats

Graf, Peter A, Satkauskas, Ignas V, King, Ryan N, Dykes, Katherine L, Quick, Julian, Kilcher, Levi F, and Rinker, Jennifer. Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation. United States: N. p., 2019. Web. doi:10.2514/6.2019-1798.
Graf, Peter A, Satkauskas, Ignas V, King, Ryan N, Dykes, Katherine L, Quick, Julian, Kilcher, Levi F, & Rinker, Jennifer. Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation. United States. doi:10.2514/6.2019-1798.
Graf, Peter A, Satkauskas, Ignas V, King, Ryan N, Dykes, Katherine L, Quick, Julian, Kilcher, Levi F, and Rinker, Jennifer. Sun . "Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation". United States. doi:10.2514/6.2019-1798.
@article{osti_1547232,
title = {Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation},
author = {Graf, Peter A and Satkauskas, Ignas V and King, Ryan N and Dykes, Katherine L and Quick, Julian and Kilcher, Levi F and Rinker, Jennifer},
abstractNote = {Estimating long return period extreme wind turbine loads is made especially difficult by the large response variability for 'the same' environmental conditions. To alleviate this, we have 'opened up the black box' of the turbulent wind generation stage of the simulations. Exploiting the notion of 'temporal coherence' allows us to manipulate the turbulent inflow to target extreme wind conditions, while at the same time quantifying 'how probable these are.' The resulting importance sampling load estimates achieve a significantly lower exceedance probability (i.e., they represent much longer return periods) than estimates using the same number of samples (i.e., the same computational resources) but only a standard Monte Carlo estimate.},
doi = {10.2514/6.2019-1798},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
Other availability
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