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Title: Active Subspaces for Wind Plant Surrogate Modeling

Abstract

Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale 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 low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces 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 surrogate-based UQ approaches tractable for utility-scale wind plants.

Authors:
 [1];  [1];  [1];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. 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 (EE-4W)
OSTI Identifier:
1433796
Report Number(s):
NREL/CP-2C00-71342
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2018 Wind Energy Symposium at the AIAA SciTech Forum, 8-12 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:10.2514/6.2018-2019.
King, Ryan N, Quick, Julian, Dykes, Katherine L, & Adcock, Christiane. Active Subspaces for Wind Plant Surrogate Modeling. United States. doi:10.2514/6.2018-2019.
King, Ryan N, Quick, Julian, Dykes, Katherine L, and Adcock, Christiane. Fri . "Active Subspaces for Wind Plant Surrogate Modeling". United States. doi:10.2514/6.2018-2019.
@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 cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale 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 low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces 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 surrogate-based UQ approaches tractable for utility-scale wind plants.},
doi = {10.2514/6.2018-2019},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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