DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads

Abstract

Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. We introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however,more » that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.« less

Authors:
 [1];  [1];  [1]; ORCiD logo [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1472240
Report Number(s):
NREL/JA-2C00-72435
Journal ID: ISSN 2366-7451
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy Science (Online)
Additional Journal Information:
Journal Name: Wind Energy Science (Online); Journal Volume: 3; Journal Issue: 2; Journal ID: ISSN 2366-7451
Publisher:
European Wind Energy Association - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 97 MATHEMATICS AND COMPUTING; wind turbines; load estimation; sampling; extreme loads

Citation Formats

Graf, Peter, Dykes, Katherine, Damiani, Rick, Jonkman, Jason, and Veers, Paul. Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads. United States: N. p., 2018. Web. doi:10.5194/wes-3-475-2018.
Graf, Peter, Dykes, Katherine, Damiani, Rick, Jonkman, Jason, & Veers, Paul. Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads. United States. https://doi.org/10.5194/wes-3-475-2018
Graf, Peter, Dykes, Katherine, Damiani, Rick, Jonkman, Jason, and Veers, Paul. Wed . "Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads". United States. https://doi.org/10.5194/wes-3-475-2018. https://www.osti.gov/servlets/purl/1472240.
@article{osti_1472240,
title = {Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads},
author = {Graf, Peter and Dykes, Katherine and Damiani, Rick and Jonkman, Jason and Veers, Paul},
abstractNote = {Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. We introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.},
doi = {10.5194/wes-3-475-2018},
journal = {Wind Energy Science (Online)},
number = 2,
volume = 3,
place = {United States},
year = {Wed Jul 11 00:00:00 EDT 2018},
month = {Wed Jul 11 00:00:00 EDT 2018}
}

Works referenced in this record:

Estimation of extreme values from sampled time series
journal, July 2009


Database for validation of design load extrapolation techniques
journal, November 2008


Assessment of Load Extrapolation Methods for Wind Turbines
journal, February 2011

  • Toft, Henrik Stensgaard; Sørensen, John Dalsgaard; Veldkamp, Dick
  • Journal of Solar Energy Engineering, Vol. 133, Issue 2
  • DOI: 10.1115/1.4003416

Towards an improved understanding of statistical extrapolation for wind turbine extreme loads
journal, November 2008

  • Fogle, Jeffrey; Agarwal, Puneet; Manuel, Lance
  • Wind Energy, Vol. 11, Issue 6
  • DOI: 10.1002/we.303

Search-based importance sampling
journal, December 1990


Computationally Efficient Uncertainty Minimization in Wind Turbine Extreme Load Assessments
journal, June 2016

  • Choe, Youngjun; Pan, Qiyun; Byon, Eunshin
  • Journal of Solar Energy Engineering, Vol. 138, Issue 4
  • DOI: 10.1115/1.4033511

The New Modularization Framework for the FAST Wind Turbine CAE Tool
conference, January 2013

  • Jonkman, Jason
  • 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
  • DOI: 10.2514/6.2013-202

Reliability analysis for a spar-supported floating offshore wind turbine
journal, August 2017


Statistical Extrapolation Methods for Estimating Wind Turbine Extreme Loads
journal, July 2008

  • Ragan, Patrick; Manuel, Lance
  • Journal of Solar Energy Engineering, Vol. 130, Issue 3
  • DOI: 10.1115/1.2931501

Advances in the Assessment of Wind Turbine Operating Extreme Loads via More Efficient Calculation Approaches
conference, January 2017

  • Graf, Peter; Damiani, Rick; Dykes, Katherine
  • 35th Wind Energy Symposium
  • DOI: 10.2514/6.2017-0680

A Stochastic Approximation Method
journal, September 1951

  • Robbins, Herbert; Monro, Sutton
  • The Annals of Mathematical Statistics, Vol. 22, Issue 3
  • DOI: 10.1214/aoms/1177729586

Towards an Improved Understanding of Statistical Extrapolation for Wind Turbine Extreme Loads
conference, June 2008

  • Fogle, Jeffrey; Agarwal, Puneet; Manuel, Lance
  • 46th AIAA Aerospace Sciences Meeting and Exhibit
  • DOI: 10.2514/6.2008-1339

Statistical Extrapolation Methods for Estimating Wind Turbine Extreme Loads
conference, June 2007

  • Ragan, Patrick; Manuel, Lance
  • 45th AIAA Aerospace Sciences Meeting and Exhibit
  • DOI: 10.2514/6.2007-1221

Assessment of Load Extrapolation Methods for Wind Turbines
conference, June 2010

  • Toft, Henrik; Sørensen, John; Veldkamp, Dick
  • 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition
  • DOI: 10.2514/6.2010-1581

Time-dependent system reliability analysis by adaptive importance sampling
journal, April 1993


Works referencing / citing this record:

OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization
journal, March 2019

  • Gray, Justin S.; Hwang, John T.; Martins, Joaquim R. R. A.
  • Structural and Multidisciplinary Optimization, Vol. 59, Issue 4
  • DOI: 10.1007/s00158-019-02211-z

Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines
journal, January 2019

  • Bortolotti, Pietro; Canet, Helena; Bottasso, Carlo L.
  • Wind Energy Science, Vol. 4, Issue 3
  • DOI: 10.5194/wes-4-397-2019

From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases
journal, January 2018

  • Dimitrov, Nikolay; Kelly, Mark C.; Vignaroli, Andrea
  • Wind Energy Science, Vol. 3, Issue 2
  • DOI: 10.5194/wes-3-767-2018