A Data-Driven Model for Wind Plant Power Optimization by Yaw Control

P. M.O. Gebraad; F. W. Teeuwisse; J. W. Van Wingerden; P. A. Fleming; S. D. Ruben; J. R. Marden; L. Y. Pao

doi:10.1109/ACC.2014.6859118

A Data-Driven Model for Wind Plant Power Optimization by Yaw Control

P. M.O. Gebraad, F. W. Teeuwisse, J. W. Van Wingerden, P. A. Fleming, S. D. Ruben, J. R. Marden, L. Y. Pao

National Wind Technology Center

Research output: Contribution to conference › Paper › peer-review

75 Scopus Citations

Abstract

This paper presents a novel parametric model that will be used to optimize the yaw settings of wind turbines in a wind plant for improved electrical energy production of the whole wind plant. The model predicts the effective steady-state flow velocities at each turbine, as well as the resulting electrical energy productions, as a function of the axial induction and the yaw angle of the different rotors. The model has a limited number of parameters that are estimated based on data. Moreover, it is shown how this model can be used to optimize the yaw settings using a game-theoretic approach. In a case study we demonstrate that our novel parametric model fits the data generated by a high-fidelity computational fluid dynamics model of a small wind plant, and that the data-driven yaw optimization control has great potential to increase the wind plant's electrical energy production.

Original language	American English
Pages	3128-3134
Number of pages	7
DOIs	https://doi.org/10.1109/ACC.2014.6859118 https://doi.org/10.1109/ACC.2014.6859118
State	Published - 2014
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: 4 Jun 2014 → 6 Jun 2014

Conference

Conference	2014 American Control Conference, ACC 2014
Country/Territory	United States
City	Portland, OR
Period	4/06/14 → 6/06/14

NREL Publication Number

NREL/CP-5000-61405

Keywords

Modeling and simulation
Optimization

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Gebraad, P. M. O., Teeuwisse, F. W., Van Wingerden, J. W., Fleming, P. A., Ruben, S. D., Marden, J. R., & Pao, L. Y. (2014). A Data-Driven Model for Wind Plant Power Optimization by Yaw Control. 3128-3134. Paper presented at 2014 American Control Conference, ACC 2014, Portland, OR, United States. https://doi.org/10.1109/ACC.2014.6859118, https://doi.org/10.1109/ACC.2014.6859118

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title = "A Data-Driven Model for Wind Plant Power Optimization by Yaw Control",

abstract = "This paper presents a novel parametric model that will be used to optimize the yaw settings of wind turbines in a wind plant for improved electrical energy production of the whole wind plant. The model predicts the effective steady-state flow velocities at each turbine, as well as the resulting electrical energy productions, as a function of the axial induction and the yaw angle of the different rotors. The model has a limited number of parameters that are estimated based on data. Moreover, it is shown how this model can be used to optimize the yaw settings using a game-theoretic approach. In a case study we demonstrate that our novel parametric model fits the data generated by a high-fidelity computational fluid dynamics model of a small wind plant, and that the data-driven yaw optimization control has great potential to increase the wind plant's electrical energy production.",

keywords = "Modeling and simulation, Optimization",

author = "Gebraad, {P. M.O.} and Teeuwisse, {F. W.} and {Van Wingerden}, {J. W.} and Fleming, {P. A.} and Ruben, {S. D.} and Marden, {J. R.} and Pao, {L. Y.}",

year = "2014",

doi = "10.1109/ACC.2014.6859118",

language = "American English",

pages = "3128--3134",

note = "2014 American Control Conference, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

}

Gebraad, PMO, Teeuwisse, FW, Van Wingerden, JW, Fleming, PA, Ruben, SD, Marden, JR & Pao, LY 2014, 'A Data-Driven Model for Wind Plant Power Optimization by Yaw Control', Paper presented at 2014 American Control Conference, ACC 2014, Portland, OR, United States, 4/06/14 - 6/06/14 pp. 3128-3134. https://doi.org/10.1109/ACC.2014.6859118, https://doi.org/10.1109/ACC.2014.6859118

TY - CONF

T1 - A Data-Driven Model for Wind Plant Power Optimization by Yaw Control

AU - Gebraad, P. M.O.

AU - Teeuwisse, F. W.

AU - Van Wingerden, J. W.

AU - Fleming, P. A.

AU - Ruben, S. D.

AU - Marden, J. R.

AU - Pao, L. Y.

PY - 2014

Y1 - 2014

N2 - This paper presents a novel parametric model that will be used to optimize the yaw settings of wind turbines in a wind plant for improved electrical energy production of the whole wind plant. The model predicts the effective steady-state flow velocities at each turbine, as well as the resulting electrical energy productions, as a function of the axial induction and the yaw angle of the different rotors. The model has a limited number of parameters that are estimated based on data. Moreover, it is shown how this model can be used to optimize the yaw settings using a game-theoretic approach. In a case study we demonstrate that our novel parametric model fits the data generated by a high-fidelity computational fluid dynamics model of a small wind plant, and that the data-driven yaw optimization control has great potential to increase the wind plant's electrical energy production.

AB - This paper presents a novel parametric model that will be used to optimize the yaw settings of wind turbines in a wind plant for improved electrical energy production of the whole wind plant. The model predicts the effective steady-state flow velocities at each turbine, as well as the resulting electrical energy productions, as a function of the axial induction and the yaw angle of the different rotors. The model has a limited number of parameters that are estimated based on data. Moreover, it is shown how this model can be used to optimize the yaw settings using a game-theoretic approach. In a case study we demonstrate that our novel parametric model fits the data generated by a high-fidelity computational fluid dynamics model of a small wind plant, and that the data-driven yaw optimization control has great potential to increase the wind plant's electrical energy production.

KW - Modeling and simulation

KW - Optimization

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U2 - 10.1109/ACC.2014.6859118

DO - 10.1109/ACC.2014.6859118

M3 - Paper

AN - SCOPUS:84905696758

SP - 3128

EP - 3134

T2 - 2014 American Control Conference, ACC 2014

Y2 - 4 June 2014 through 6 June 2014

ER -

A Data-Driven Model for Wind Plant Power Optimization by Yaw Control

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