Computationally Efficient Uncertainty Minimization in Wind Turbine Extreme Load Assessments

Choe, Youngjun; Pan, Qiyun; Byon, Eunshin

doi:10.1115/1.4033511

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…whereŷ k andŷ k, can be obtained using order statistics among the outputs obtained up to the current iteration (Choe, Pan, & Byon, 2016). Any of these two estimates can be used as the next density parameter, k + 1 .…”

Section: Adaptive Importance Samplingmentioning

confidence: 99%

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Pan

Byon

et al. 2020

Naval Research Logistics

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Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.

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Section: Adaptive Importance Samplingmentioning

confidence: 99%

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Pan

Byon

et al. 2020

Naval Research Logistics

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“…NREL's WISDEM software allows for the execution of FAST and its companion tool TurbSim (which generates turbulent wind fields for input to FAST) in a programmatic fashion from Python, as has been reported previously (Graf et al, 2016). The particular turbine on which we are testing these methods is the NREL 5 MW reference turbine, often used for such studies (Jonkman et al, 2015;Choe et al, 2016), in an onshore configuration. The environmental conditions are thus described by hub height mean wind speed (modeled by a Weibull distribution with scale and shape parameters of 11.28 m s −1 and 2, respectively).…”

Section: Turbine Simulationmentioning

confidence: 99%

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

et al. 2018

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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.Published by Copernicus Publications on behalf of the European Academy of Wind Energy e.V.

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“…Let y j denote the j th output of response Y and N T denote the total number of simulation runs. Using the CMC, the POE estimator at level (6) and the extreme load estimator corresponding to the target POE level P T is given bŷ l T,CM C = inf{θ ∈ R :…”

Section: Problem Backgroundmentioning

confidence: 99%

“…where "inf" represents the infimum. 6 Mathematically, the extreme load l T relates to the extreme quantile associated with the right tail probability P T in the distribution of Y (Figure 1). When P T is small, estimating the extreme quantile faces large estimation uncertainties and computational difficulties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Extreme Load Estimation in Wind Turbines

Pan

Byon²

2017

35th Wind Energy Symposium

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To help wind turbine reliability analysis in a design stage, aeroelastic simulators have been developed to generate stochastic load responses imposed on a wind turbine structure, given pre-specified turbulent wind conditions. In particular, system designers can use the simulators to estimate the extreme load responses during a turbine's design life. However, it has been shown that using the crude Monte Carlo sampling methods to simulate the extreme load associated with a small load exceeding probability is computationally prohibitive, and the estimation results are highly uncertain. Importance sampling methods can overcome the limitations of the crude Monte Carlo sampling methods. We develop adaptive algorithms to iteratively refine the importance sampling density to efficiently estimate the extreme load. Nomenclature P T Target probability of load exceedance l T Extreme load X Input vector Y Output variable p X Original input density of X q X Importance sampling density θ Parameter in the importance sampling density

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Computationally Efficient Uncertainty Minimization in Wind Turbine Extreme Load Assessments

Cited by 20 publications

References 19 publications

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

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

Adaptive Extreme Load Estimation in Wind Turbines

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