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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, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. 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; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
OSTI Identifier:
1677495
Report Number(s):
NREL/JA-5000-78105
Journal ID: ISSN 2366-7451; MainId:32014;UUID:37a60eed-8a12-4a9f-8c58-6592d6e5d180;MainAdminID:18704
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy Science (Online)
Additional Journal Information:
Journal Name: Wind Energy Science (Online); Journal Volume: 5; Journal Issue: 2; Journal ID: ISSN 2366-7451
Publisher:
European Wind Energy Association - Copernicus
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, Fleming, Paul, and King, Jennifer. Design and analysis of a wake steering controller with wind direction variability. United States: N. p., 2020. Web. https://doi.org/10.5194/wes-5-451-2020.
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Simley, Eric, Fleming, Paul, & King, Jennifer. Design and analysis of a wake steering controller with wind direction variability. United States. https://doi.org/10.5194/wes-5-451-2020
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Simley, Eric, Fleming, Paul, and King, Jennifer. Wed . "Design and analysis of a wake steering controller with wind direction variability". United States. https://doi.org/10.5194/wes-5-451-2020. https://www.osti.gov/servlets/purl/1677495.
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@article{osti_1677495,
title = {Design and analysis of a wake steering controller with wind direction variability},
author = {Simley, Eric and Fleming, Paul and King, Jennifer},
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, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. 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-5-451-2020},
journal = {Wind Energy Science (Online)},
number = 2,
volume = 5,
place = {United States},
year = {2020},
month = {4}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.5194/wes-5-451-2020
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Figures / Tables:

Figure 1 Figure 1: Examples of wakes for a two-turbine scenario with five rotor diameter ($D$) spacing using FLORIS. In the baseline case, both turbines are aligned with the wind direction. For the offset case, the upstream turbine has a yaw offset of 20°.
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Works referenced in this record:

Assessment of wind turbine component loads under yaw-offset conditions
journal, January 2018

  • Damiani, Rick; Dana, Scott; Annoni, Jennifer
  • Wind Energy Science, Vol. 3, Issue 1
  • DOI: 10.5194/wes-3-173-2018

Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control
journal, January 2019

  • Mendez Reyes, Hector; Kanev, Stoyan; Doekemeijer, Bart
  • Wind Energy Science, Vol. 4, Issue 4
  • DOI: 10.5194/wes-4-549-2019

Experimental and theoretical study of wind turbine wakes in yawed conditions
journal, October 2016

  • Bastankhah, Majid; Porté-Agel, Fernando
  • Journal of Fluid Mechanics, Vol. 806
  • DOI: 10.1017/jfm.2016.595

Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm
journal, May 2013

  • Rajewski, Daniel A.; Takle, Eugene S.; Lundquist, Julie K.
  • Bulletin of the American Meteorological Society, Vol. 94, Issue 5
  • DOI: 10.1175/BAMS-D-11-00240.1

Analysis of control-oriented wake modeling tools using lidar field results
journal, January 2018

  • Annoni, Jennifer; Fleming, Paul; Scholbrock, Andrew
  • Wind Energy Science, Vol. 3, Issue 2
  • DOI: 10.5194/wes-3-819-2018

A new analytical model for wind-turbine wakes
journal, October 2014

Wind tunnel testing of wake control strategies
conference, July 2016

  • Campagnolo, Filippo; Petrovic, Vlaho; Bottasso, Carlo L.
  • 2016 American Control Conference (ACC)
  • DOI: 10.1109/ACC.2016.7524965

Robust active wake control in consideration of wind direction variability and uncertainty
journal, January 2018

  • Rott, Andreas; Doekemeijer, Bart; Seifert, Janna Kristina
  • Wind Energy Science, Vol. 3, Issue 2
  • DOI: 10.5194/wes-3-869-2018

Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study
journal, January 2016

  • Vollmer, Lukas; Steinfeld, Gerald; Heinemann, Detlev
  • Wind Energy Science, Vol. 1, Issue 2
  • DOI: 10.5194/wes-1-129-2016

A Large-Eddy Simulations of Wind-Plant Aerodynamics
conference, November 2012

  • Churchfield, Matthew; Lee, Sang; Moriarty, Patrick
  • 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
  • DOI: 10.2514/6.2012-537

Evaluating techniques for redirecting turbine wakes using SOWFA
journal, October 2014

Optimization Under Uncertainty for Wake Steering Strategies
journal, May 2017

Wind farm control: Addressing the aerodynamic interaction among wind turbines
conference, June 2009

Analytical Modeling of Wind Farms: A New Approach for Power Prediction
journal, September 2016

  • Niayifar, Amin; Porté-Agel, Fernando
  • Energies, Vol. 9, Issue 9
  • DOI: 10.3390/en9090741

A simulation study demonstrating the importance of large-scale trailing vortices in wake steering
journal, January 2018

  • Fleming, Paul; Annoni, Jennifer; Churchfield, Matthew
  • Wind Energy Science, Vol. 3, Issue 1
  • DOI: 10.5194/wes-3-243-2018

A tutorial on control-oriented modeling and control of wind farms
conference, May 2017

Field test of wake steering at an offshore wind farm
journal, January 2017

  • Fleming, Paul; Annoni, Jennifer; Shah, Jigar J.
  • Wind Energy Science, Vol. 2, Issue 1
  • DOI: 10.5194/wes-2-229-2017

Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1
journal, January 2019

  • Fleming, Paul; King, Jennifer; Dykes, Katherine
  • Wind Energy Science, Vol. 4, Issue 2
  • DOI: 10.5194/wes-4-273-2019

Wake steering via yaw control in multi-turbine wind farms: Recommendations based on large-eddy simulation
journal, June 2019

  • Archer, Cristina L.; Vasel-Be-Hagh, Ahmad
  • Sustainable Energy Technologies and Assessments, Vol. 33
  • DOI: 10.1016/j.seta.2019.03.002

Wind direction estimation using SCADA data with consensus-based optimization
journal, January 2019

  • Annoni, Jennifer; Bay, Christopher; Johnson, Kathryn
  • Wind Energy Science, Vol. 4, Issue 2
  • DOI: 10.5194/wes-4-355-2019

Combining induction control and wake steering for wind farm energy and fatigue loads optimisation
journal, June 2018

Wind tunnel tests on controllable model wind turbines in yaw
conference, January 2016

  • Schottler, Jannik; Hölling, Agnieszka; Peinke, Joachim
  • 34th Wind Energy Symposium
  • DOI: 10.2514/6.2016-1523

Turbine Inflow Characterization at the National Wind Technology Center
journal, May 2013

  • Clifton, Andrew; Schreck, Scott; Scott, George
  • Journal of Solar Energy Engineering, Vol. 135, Issue 3
  • DOI: 10.1115/1.4024068

Simulation of Stochastic Processes by Spectral Representation
journal, April 1991

  • Shinozuka, Masanobu; Deodatis, George
  • Applied Mechanics Reviews, Vol. 44, Issue 4
  • DOI: 10.1115/1.3119501

The aerodynamics of the curled wake: a simplified model in view of flow control
journal, January 2019

  • Martínez-Tossas, Luis A.; Annoni, Jennifer; Fleming, Paul A.
  • Wind Energy Science, Vol. 4, Issue 1
  • DOI: 10.5194/wes-4-127-2019
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