IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2017i1p53-d124616.html
   My bibliography  Save this article

Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

Author

Listed:
  • Jae Sang Moon

    (Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712, USA)

  • Lance Manuel

    (Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712, USA)

  • Matthew J. Churchfield

    (National Renewable Energy Laboratory, Golden, CO 80303, USA)

  • Sang Lee

    (Department of Mechanical Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

  • Paul S. Veers

    (National Renewable Energy Laboratory, Golden, CO 80303, USA)

Abstract

Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study’s overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.

Suggested Citation

  • Jae Sang Moon & Lance Manuel & Matthew J. Churchfield & Sang Lee & Paul S. Veers, 2017. "Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads," Energies, MDPI, vol. 11(1), pages 1-34, December.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:53-:d:124616
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/53/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/53/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feng, Dachuan & Gupta, Vikrant & Li, Larry K.B. & Wan, Minping, 2024. "An improved dynamic model for wind-turbine wake flow," Energy, Elsevier, vol. 290(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    2. Jewson Stephen & Penzer Jeremy, 2006. "Estimating Trends in Weather Series: Consequences for Pricing Derivatives," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-17, September.
    3. Luebke, Karsten & Czogiel, Irina & Weihs, Claus, 2004. "Latent Factor Prediction Pursuit for Rank Deficient Regressors," Technical Reports 2004,75, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Wang, Yihe & Zhao, Sihai Dave, 2021. "A nonparametric empirical Bayes approach to large-scale multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    5. Adel Javanmard & Jingwei Ji & Renyuan Xu, 2024. "Multi-Task Dynamic Pricing in Credit Market with Contextual Information," Papers 2410.14839, arXiv.org, revised Oct 2024.
    6. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    7. Qiang Sun & Hongtu Zhu & Yufeng Liu & Joseph G. Ibrahim, 2015. "SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 289-302, March.
    8. Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & João Ricardo Sato, 2021. "Assessing the educational performance of different Brazilian school cycles using data science methods," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
    9. Jhun, Myoungshic & Choi, Inkyung, 2009. "Bootstrapping least distance estimator in the multivariate regression model," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4221-4227, October.
    10. Jeongsub Choi & Mengmeng Zhu & Jihoon Kang & Myong K. Jeong, 2024. "Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing," Annals of Operations Research, Springer, vol. 339(1), pages 185-201, August.
    11. Simila, Timo & Tikka, Jarkko, 2007. "Input selection and shrinkage in multiresponse linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 406-422, September.
    12. Shih-Hao Huang & Hsin-Cheng Huang & Ruey S. Tsay & Guangming Pan, 2021. "Testing Independence Between Two Spatial Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 161-179, June.
    13. Flandoli, F. & Giorgi, E. & Aspinall, W.P. & Neri, A., 2011. "Comparison of a new expert elicitation model with the Classical Model, equal weights and single experts, using a cross-validation technique," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1292-1310.
    14. Alberto Ferrer & Daniel Aguado & Santiago Vidal‐Puig & José Manuel Prats & Manuel Zarzo, 2008. "PLS: A versatile tool for industrial process improvement and optimization," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(6), pages 551-567, November.
    15. Gawusu, Sidique & Ahmed, Abubakari, 2024. "Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach," Energy, Elsevier, vol. 304(C).
    16. ter Braak, Cajo J.F., 2006. "Bayesian sigmoid shrinkage with improper variance priors and an application to wavelet denoising," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1232-1242, November.
    17. Bhaumik, Dulal K. & Nordgren, Rachel K., 2019. "Prediction and calibration for multiple correlated variables," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 313-327.
    18. Gabriel Borrageiro, 2022. "Sequential asset ranking in nonstationary time series," Papers 2202.12186, arXiv.org, revised Oct 2022.
    19. Srivastava, M. S. & Kubokawa, T., 2005. "Minimax multivariate empirical Bayes estimators under multicollinearity," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 394-416, April.
    20. Lee, Wonyul & Liu, Yufeng, 2012. "Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 241-255.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:53-:d:124616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.