Active Subspaces for Wind Plant Surrogate Modeling
Abstract
Understanding the uncertainty in wind plant performance is crucial to their costeffective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utilityscale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a lowdimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active subspaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogatebased UQ approaches tractable for utilityscale wind plants.
 Authors:

 National Renewable Energy Laboratory (NREL), Golden, CO (United States)
 Massachusetts Institute of Technology
 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 (EE4W)
 OSTI Identifier:
 1433796
 Report Number(s):
 NREL/CP2C0071342
 DOE Contract Number:
 AC3608GO28308
 Resource Type:
 Conference
 Resource Relation:
 Conference: Presented at the 2018 Wind Energy Symposium at the AIAA SciTech Forum, 812 January 2018, Kissimmee, Florida
 Country of Publication:
 United States
 Language:
 English
 Subject:
 17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; fuel additives; Monte Carlo methods; wind power; uncertainty analysis
Citation Formats
King, Ryan N, Quick, Julian, Dykes, Katherine L, and Adcock, Christiane. Active Subspaces for Wind Plant Surrogate Modeling. United States: N. p., 2018.
Web. doi:10.2514/6.20182019.
King, Ryan N, Quick, Julian, Dykes, Katherine L, & Adcock, Christiane. Active Subspaces for Wind Plant Surrogate Modeling. United States. doi:10.2514/6.20182019.
King, Ryan N, Quick, Julian, Dykes, Katherine L, and Adcock, Christiane. Fri .
"Active Subspaces for Wind Plant Surrogate Modeling". United States. doi:10.2514/6.20182019.
@article{osti_1433796,
title = {Active Subspaces for Wind Plant Surrogate Modeling},
author = {King, Ryan N and Quick, Julian and Dykes, Katherine L and Adcock, Christiane},
abstractNote = {Understanding the uncertainty in wind plant performance is crucial to their costeffective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utilityscale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a lowdimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active subspaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogatebased UQ approaches tractable for utilityscale wind plants.},
doi = {10.2514/6.20182019},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}
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