Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates
Abstract
Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertainty models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.
- Authors:
-
[1];
- Technical Univ. of Denmark, Roskilde (Denmark). Dept. of Wind Energy
- Technical Univ. of Denmark, Roskilde (Denmark). Dept. of Wind Energy; Aalborg Univ., Aalborg (Denmark)
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- OSTI Identifier:
- 1411128
- Report Number(s):
- NREL/JA-2C00-70570
Journal ID: ISSN 0960-1481
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Renewable Energy
- Additional Journal Information:
- Journal Volume: 119; Journal Issue: C; Journal ID: ISSN 0960-1481
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 17 WIND ENERGY; wind energy; uncertainty quantification; aeroelastic wind turbine model; annual energy production; lifetime equivalent fatigue loads
Citation Formats
Murcia, Juan Pablo, Réthoré, Pierre-Elouan, Dimitrov, Nikolay, Natarajan, Anand, Sørensen, John Dalsgaard, Graf, Peter, and Kim, Taeseong. Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates. United States: N. p., 2017.
Web. doi:10.1016/j.renene.2017.07.070.
Murcia, Juan Pablo, Réthoré, Pierre-Elouan, Dimitrov, Nikolay, Natarajan, Anand, Sørensen, John Dalsgaard, Graf, Peter, & Kim, Taeseong. Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates. United States. https://doi.org/10.1016/j.renene.2017.07.070
Murcia, Juan Pablo, Réthoré, Pierre-Elouan, Dimitrov, Nikolay, Natarajan, Anand, Sørensen, John Dalsgaard, Graf, Peter, and Kim, Taeseong. Mon .
"Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates". United States. https://doi.org/10.1016/j.renene.2017.07.070. https://www.osti.gov/servlets/purl/1411128.
@article{osti_1411128,
title = {Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates},
author = {Murcia, Juan Pablo and Réthoré, Pierre-Elouan and Dimitrov, Nikolay and Natarajan, Anand and Sørensen, John Dalsgaard and Graf, Peter and Kim, Taeseong},
abstractNote = {Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertainty models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.},
doi = {10.1016/j.renene.2017.07.070},
journal = {Renewable Energy},
number = C,
volume = 119,
place = {United States},
year = {Mon Jul 17 00:00:00 EDT 2017},
month = {Mon Jul 17 00:00:00 EDT 2017}
}
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Works referenced in this record:
High-throughput computation and the applicability of Monte Carlo integration in fatigue load estimation of floating offshore wind turbines: Monte Carlo-based fatigue load estimation
journal, July 2015
- Graf, Peter A.; Stewart, Gordon; Lackner, Matthew
- Wind Energy, Vol. 19, Issue 5
Assessment of wind turbine structural integrity using response surface methodology
journal, January 2016
- Toft, Henrik Stensgaard; Svenningsen, Lasse; Moser, Wolfgang
- Engineering Structures, Vol. 106
Influence of the control system on wind turbine loads during power production in extreme turbulence: Structural reliability
journal, March 2016
- Abdallah, I.; Natarajan, A.; Sørensen, J. D.
- Renewable Energy, Vol. 87
Using machine learning to predict wind turbine power output
journal, April 2013
- Clifton, A.; Kilcher, L.; Lundquist, J. K.
- Environmental Research Letters, Vol. 8, Issue 2
Effect of winds in a mountain pass on turbine performance: Mountain pass winds
journal, August 2013
- Clifton, A.; Daniels, M. H.; Lehning, M.
- Wind Energy, Vol. 17, Issue 10
Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
journal, January 2004
- Soize, Christian; Ghanem, Roger
- SIAM Journal on Scientific Computing, Vol. 26, Issue 2
Uncertainty propagation using Wiener–Haar expansions
journal, June 2004
- Le Maı̂tre, O. P.; Knio, O. M.; Najm, H. N.
- Journal of Computational Physics, Vol. 197, Issue 1
Polynomial Chaos Expansion with Latin Hypercube Sampling for Estimating Response Variability
journal, June 2004
- Choi, Seung-Kyum; Grandhi, Ramana V.; Canfield, Robert A.
- AIAA Journal, Vol. 42, Issue 6
High-Order Collocation Methods for Differential Equations with Random Inputs
journal, January 2005
- Xiu, Dongbin; Hesthaven, Jan S.
- SIAM Journal on Scientific Computing, Vol. 27, Issue 3
Adaptive sparse polynomial chaos expansion based on least angle regression
journal, March 2011
- Blatman, Géraud; Sudret, Bruno
- Journal of Computational Physics, Vol. 230, Issue 6
Database for validation of design load extrapolation techniques
journal, November 2008
- Moriarty, Patrick
- Wind Energy, Vol. 11, Issue 6
Outlier robustness for wind turbine extrapolated extreme loads: Robust extrapolated extreme loads
journal, November 2011
- Natarajan, Anand; Verelst, David R.
- Wind Energy, Vol. 15, Issue 5
Remarks on a Multivariate Transformation
journal, September 1952
- Rosenblatt, Murray
- The Annals of Mathematical Statistics, Vol. 23, Issue 3
Algorithm 726; ORTHPOL---a package of routines for generating orthogonal polynomials and Gauss-type quadrature rules
journal, March 1994
- Gautschi, Walter
- ACM Transactions on Mathematical Software, Vol. 20, Issue 1
Model of wind shear conditional on turbulence and its impact on wind turbine loads: Model of wind shear conditional on turbulence
journal, August 2014
- Dimitrov, Nikolay; Natarajan, Anand; Kelly, Mark
- Wind Energy, Vol. 18, Issue 11
Chaospy: An open source tool for designing methods of uncertainty quantification
journal, November 2015
- Feinberg, Jonathan; Langtangen, Hans Petter
- Journal of Computational Science, Vol. 11
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
journal, February 2000
- Mckay, M. D.; Beckman, R. J.; Conover, W. J.
- Technometrics, Vol. 42, Issue 1
Monte Carlo Methods for Solving Multivariable Problems
journal, May 1960
- Hammersley, J. M.
- Annals of the New York Academy of Sciences, Vol. 86, Issue 3
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
journal, February 2001
- Sobol′, I. M.
- Mathematics and Computers in Simulation, Vol. 55, Issue 1-3
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
journal, February 2010
- Saltelli, Andrea; Annoni, Paola; Azzini, Ivano
- Computer Physics Communications, Vol. 181, Issue 2
Global sensitivity analysis using polynomial chaos expansions
journal, July 2008
- Sudret, Bruno
- Reliability Engineering & System Safety, Vol. 93, Issue 7
Wind field simulation
journal, October 1998
- Mann, Jakob
- Probabilistic Engineering Mechanics, Vol. 13, Issue 4
Works referencing / citing this record:
Effective turbulence and its implications in wind turbine fatigue assessment
journal, December 2019
- Slot, René M. M.; Sørensen, John D.; Svenningsen, Lasse
- Wind Energy, Vol. 22, Issue 12
Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations
journal, June 2018
- Schröder, Laura; Krasimirov Dimitrov, Nikolay; Verelst, David Robert
- Journal of Physics: Conference Series, Vol. 1037
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
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