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

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 themore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1567028
Report Number(s):
NREL/JA-5000-74098
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Wind Energy Science Discussions
Additional Journal Information:
Journal Name: Wind Energy Science Discussions
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind energy; wind farm control; wake steering; robust control; wind direction uncertainty

Citation Formats

Simley, Eric J, Fleming, Paul A, and King, Jennifer R. Design and Analysis of a Wake Steering Controller with Wind Direction Variability. United States: N. p., 2019. Web. doi:10.5194/wes-2019-35.
Simley, Eric J, Fleming, Paul A, & King, Jennifer R. Design and Analysis of a Wake Steering Controller with Wind Direction Variability. United States. doi:10.5194/wes-2019-35.
Simley, Eric J, Fleming, Paul A, and King, Jennifer R. Tue . "Design and Analysis of a Wake Steering Controller with Wind Direction Variability". United States. doi:10.5194/wes-2019-35.
@article{osti_1567028,
title = {Design and Analysis of a Wake Steering Controller with Wind Direction Variability},
author = {Simley, Eric J and Fleming, Paul A and King, Jennifer R},
abstractNote = {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.},
doi = {10.5194/wes-2019-35},
journal = {Wind Energy Science Discussions},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {7}
}