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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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CiteScore value: 0.6
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Preprints
https://doi.org/10.5194/wes-2020-52
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-52
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 Mar 2020

09 Mar 2020

Review status
A revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Michael F. Howland1, Aditya S. Ghate2, Sanjiva K. Lele1,2, and John O. Dabiri3,4 Michael F. Howland et al.
  • 1Department of Mechanical Engineering, Stanford University, Stanford, CA 94305
  • 2Department of Astronautics and Aeronautics, Stanford University, Stanford, CA 94305
  • 3Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT), California Institute of Technology, Pasadena, CA 91125
  • 4Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA 91125

Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

Michael F. Howland et al.

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Michael F. Howland et al.

Michael F. Howland et al.

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Latest update: 23 Sep 2020
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Short summary
Wake losses significantly reduce the power production of utility-scale wind farms since all wind turbines are operated in a greedy, individual power maximization fashion. In order to mitigate wake losses, collective wind farm operation strategies use wake steering, where certain turbines are intentionally misaligned with respect to the incoming wind direction. The control strategy developed is dynamic and closed-loop to adapt to changing atmospheric conditions.
Wake losses significantly reduce the power production of utility-scale wind farms since all wind...
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