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

Research output: Contribution to conferencePaperpeer-review

70 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 languageAmerican English
Pages3128-3134
Number of pages7
DOIs
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: 4 Jun 20146 Jun 2014

Conference

Conference2014 American Control Conference, ACC 2014
Country/TerritoryUnited States
CityPortland, OR
Period4/06/146/06/14

NREL Publication Number

  • NREL/CP-5000-61405

Keywords

  • Modeling and simulation
  • Optimization

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