Adaptive Stratified Importance Sampling: Hybridization of Extrapolation and Importance Sampling Monte Carlo Methods for Estimation of Wind Turbine Extreme Loads

Peter Graf, Jason Jonkman, Paul Veers, Katherine Dykes, Rick Damiani

Research output: Contribution to journalArticlepeer-review

17 Scopus Citations

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. Here 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.

Original languageAmerican English
Pages (from-to)475-487
Number of pages13
JournalWind Energy Science
Volume3
Issue number2
DOIs
StatePublished - 2018

Bibliographical note

See NREL/JA-5000-72638 for article as published in Wind Energy Science Discussions

NREL Publication Number

  • NREL/JA-2C00-72435

Keywords

  • extreme loads
  • load estimation
  • sampling
  • wind turbines

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