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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2019-35
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-35
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 23 Jul 2019

Submitted as: research article | 23 Jul 2019

Review status
This preprint is currently under review for the journal WES.

Design and Analysis of a Wake Steering Controller with Wind Direction Variability

Eric Simley, Paul Fleming, and Jennifer King Eric Simley et al.
  • National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA

Abstract. Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values. For a spacing of 5 rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain improvement of 128 %. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

Eric Simley et al.

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Status: final response (author comments only)
Status: final response (author comments only)
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Eric Simley et al.

Eric Simley et al.

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Latest update: 16 Feb 2020
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Short summary
Wind farm wake losses occur when turbines operate in the wakes of upstream turbines. However, wake steering control can be used to deflect wakes away from downstream turbines. A method for including wind direction variability in wake steering simulations is presented here. Controller performance is shown to improve when wind direction variability is accounted for. Furthermore, the importance of wind direction variability is shown for different turbine spacings and atmospheric conditions.
Wind farm wake losses occur when turbines operate in the wakes of upstream turbines. However,...
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