This document discusses research into wake steering techniques for improved wind plant performance. It summarizes research using computational fluid dynamics (CFD) modeling and engineering models to simulate different wake steering control strategies. Field tests were also conducted to validate wake modeling and investigate wake steering feasibility. The research found that intentional wake steering is feasible, atmospheric stability significantly impacts wakes, and CFD simulations helped guide field experiment design, but more work is still needed to fully validate wake models with field data and quantify turbine interactions.

1. Wake Steering for Improved
Wind Plant Performance
Andrew Scholbrock, NREL
2016 Wind Turbine Blade Workshop
Albuquerque, NM, USA
August 31, 2016

2. 2
Problem
Photo by: Christian Steiness

3. 3
Research Methodology
CFD Modeling of
Atmosphere/
Wind Plant
Simplified
Engineering
Models of Wakes
Wind Plant
Optimization
Simulations
Field Testing
Validation of
Wake Models
Field Testing
Turbine
Interactions
Coordinated Wind
Plant Control

4. 4
• SOWFA: Simulator fOr
Wind Farm Applications
o Used to investigate wind
turbine and wind plant
performance under
various atmospheric
conditions
o LES model of atmospheric
boundary layer (ABL)
based on OpenFOAMCFD
toolbox
o FAST turbine model using
rotating actuator lines to
model rotor
CFD Modeling
Adapted From: J. Annoni et. al. “Analysis of axial-induction-based wind plant
control using an engineering and a high-order wind plant model” Wind Energy.
Vol. 19. pp. 1135-1150. DOI: 10.1002/we.1891. 2016.
More information:nwtc.nrel.gov/SOWFA

5. 5
Wake Engineering Model
• FLORIS (FLOw Redirection and Induction in Steady-state):
o Steady-state engineering model based on Jensen and
Jimenez models with extensions

6. 6
Methods for wake manipulation
o Axial-based control
o Wake Steering:
– Repositioning (layout optimization)
– Tilt-based wake-steering
– Yaw-based
Adapted From: J. Annoni et. al. “Analysis of axial-induction-based wind plant control using an engineering
and a high-order wind plant model” Wind Energy. Vol. 19. pp. 1135-1150. DOI: 10.1002/we.1891.
2016.

7. 7
Axial Based Method – Simulation Results
Adapted From: J. Annoni et. al. “Analysis of axial-induction-based wind plant control using an engineering and a
high-order wind plant model” Wind Energy. Vol. 19. pp. 1135-1150. DOI: 10.1002/we.1891. 2016.

8. 8
Wake Steering – Initial CFD Investigations
Adapted From: P. Fleming, et. al. “Simulation comparison of wake mitigation control strategies for a two-turbine case” Wind Energy.
Vol. 18. pp. 2135-2143 DOI: 10.1002/we.1810. 2014.

9. 9
Wind Plant Optimization Simulation Results
Adapted From: Gebraad, P. M. O.; Teeuwisse, F. W.; van Wingerden, J. W.; Fleming, P. A.; Ruben, S. D.; Marden, J. R.; Pao, L. Y. (2014). “Data-
Driven Model for Wind Plant Power Optimization by Yaw Control.” Proceedings of the 2014 American Control Conference (ACC); June 4-6, 2014,
Portland, Oregon. NREL/CP-5000-61405. Piscataway, NJ: Institute of Electrical and Electronics Engineers; pp. 3128-3134.

10. 10
Field Testing
More information on SWiFT: energy.sandia.gov/energy/renewable-energy/wind-power/wind_plant_opt/
More information on NWTC: nwtc.nrel.gov
More information on DTU Lidar: www.windscanner.dk/
More information on SWE Lidar: www.ifb.uni-stuttgart.de/windenergie/index.en.html
Scaled Wind Farm TechnologyFacility
(SWiFT), Lubbock, Texas
Photo by Thomas Herges, Sandia National Laboratory Photo by Dennis Schroeder, NREL
National Wind Technology Center
(NWTC), Boulder, Colorado
• Wind Turbine: Vestas V27
• Lidar: Danish Technical University (DTU) modified
ZephIR
• Wind Turbine: GE 1.5 MW
• Lidar: University of Stuttgart (SWE) modified
Leosphere

11. 11
Lidar pattern sampled CFD data
Simulated Lidar Sampling of Yawed Wake for SWiFT
(Courtesy Matt Churchfield – NREL)
q = 30° wind direction
g = -20°
b = -10°
Pure CFD data
Data reconstructed from lidar sampling

12. 12
Lidar sampling at different ranges
Image courtesy of Tommy Herges, Sandia National Laboratory

13. 13
Simulated Lidar Sampling of Wake for GE 1.5
(Courtesy Matt Churchfield – NREL)

14. 14
GE 1.5 Field Test Wake Steering
Wake behind GE 1.5
Aligned with Wind Direction
Wake behind GE 1.5
+25ᵒ Yaw Misalignment
Images courtesy of Jennifer Annoni, NREL

15. 15
SWiFT Field test results – Stable ABL
Wake behind SWiFT wind turbine
Stable Atmospheric Boundary Layer
Video courtesy of Tommy Herges, Sandia National Laboratory

16. 16
SWiFT Field test results – Unstable ABL
Wake behind SWiFT wind turbine
Unstable Atmospheric Boundary Layer
Video courtesy of Tommy Herges, Sandia National Laboratory

17. 17
• CFD simulations helped immensely in guiding the
design of field experiments
• Intentional wake steering is feasible from field
experiments
• Atmospheric stability plays a large role in wake
meandering and needs to be taken into account for
coordinated wind farm control
• Need to quantify “steered” wake from field tests and
compare to simulation models for validation
• Need to quantify turbine to turbine interactions
Conclusions

18. Thank you for your time!
Photo by: Dennis Schroeder, NREL