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Title: Field Test of Wake Steering at an Offshore Wind Farm

Abstract

In this paper, a field test of wake steering control is presented. The field test is the result of a collaboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy management company and turbine manufacturer. In the campaign, an array of turbines within an operating commercial offshore wind farm in China have the normal yaw controller modified to implement wake steering according to a yaw control strategy. The strategy was designed using NREL wind farm models, including a computational fluid dynamics model, SOWFA, for understanding wake dynamics and an engineering model, FLORIS, for yaw control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture, by amounts similar to those predicted from the models.

Authors:
ORCiD logo; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1343393
Report Number(s):
NREL/JA-5000-67623
Journal ID: ISSN 2366-7621
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy Science Discussions
Additional Journal Information:
Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2366-7621
Publisher:
European Academy of Wind Energy - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind farm control; wind energy; field testing; wake steering

Citation Formats

Fleming, Paul, Annoni, Jennifer, Shah, Jigar J., Wang, Linpeng, Ananthan, Shreyas, Zhang, Zhijun, Hutchings, Kyle, Wang, Peng, Chen, Weiguo, and Chen, Lin. Field Test of Wake Steering at an Offshore Wind Farm. United States: N. p., 2017. Web. doi:10.5194/wes-2017-4.
Fleming, Paul, Annoni, Jennifer, Shah, Jigar J., Wang, Linpeng, Ananthan, Shreyas, Zhang, Zhijun, Hutchings, Kyle, Wang, Peng, Chen, Weiguo, & Chen, Lin. Field Test of Wake Steering at an Offshore Wind Farm. United States. https://doi.org/10.5194/wes-2017-4
Fleming, Paul, Annoni, Jennifer, Shah, Jigar J., Wang, Linpeng, Ananthan, Shreyas, Zhang, Zhijun, Hutchings, Kyle, Wang, Peng, Chen, Weiguo, and Chen, Lin. Mon . "Field Test of Wake Steering at an Offshore Wind Farm". United States. https://doi.org/10.5194/wes-2017-4. https://www.osti.gov/servlets/purl/1343393.
@article{osti_1343393,
title = {Field Test of Wake Steering at an Offshore Wind Farm},
author = {Fleming, Paul and Annoni, Jennifer and Shah, Jigar J. and Wang, Linpeng and Ananthan, Shreyas and Zhang, Zhijun and Hutchings, Kyle and Wang, Peng and Chen, Weiguo and Chen, Lin},
abstractNote = {In this paper, a field test of wake steering control is presented. The field test is the result of a collaboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy management company and turbine manufacturer. In the campaign, an array of turbines within an operating commercial offshore wind farm in China have the normal yaw controller modified to implement wake steering according to a yaw control strategy. The strategy was designed using NREL wind farm models, including a computational fluid dynamics model, SOWFA, for understanding wake dynamics and an engineering model, FLORIS, for yaw control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture, by amounts similar to those predicted from the models.},
doi = {10.5194/wes-2017-4},
journal = {Wind Energy Science Discussions},
number = 1,
volume = 2,
place = {United States},
year = {Mon Feb 06 00:00:00 EST 2017},
month = {Mon Feb 06 00:00:00 EST 2017}
}

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Works referencing / citing this record:

Full-Scale Field Test of Wake Steering
journal, May 2017


Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes
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A review of applications of artificial intelligent algorithms in wind farms
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Modulation of turbulence scales passing through the rotor of a wind turbine
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Investigation into the shape of a wake of a yawed full-scale turbine
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Study of wind farm control potential based on SCADA data
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Coplanar lidar measurement of a single wind energy converter wake in distinct atmospheric stability regimes at the Perdigão 2017 experiment
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Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades
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Wake Management in Wind Farms: An Adaptive Control Approach
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Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms
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An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer
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Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1
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text, January 2018


Analysis of Wind-Turbine Main Bearing Loads Due to Constant Yaw Misalignments over a 20 Years Timespan
text, January 2019


Wind tunnel experiments on wind turbine wakes in yaw: Redefining the wake width
posted_content, January 2018

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  • Wind Energy Science Discussions
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Analysis of control-oriented wake modeling tools using lidar field results
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Robust active wake control in consideration of wind direction variability and uncertainty
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Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm
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Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control
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text, January 2018


Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1
posted_content, February 2019

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Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models
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Mechanical behaviour of wind turbines operating above design conditions
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Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades
journal, December 2018

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  • Applied Sciences, Vol. 8, Issue 12
  • DOI: 10.3390/app8122639

Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation
journal, December 2020


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preprint, January 2018


The helix approach: using dynamic individual pitch control to enhance wake mixing in wind farms
preprint, January 2019


Expert Elicitation on Wind Farm Control
text, January 2020


Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1
posted_content, February 2019

  • Fleming, Paul; King, Jennifer; Dykes, Katherine
  • Wind Energy Science Discussions
  • DOI: 10.5194/wes-2019-5

Field testing of a local wind inflow estimator and wake detector
posted_content, March 2020

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  • Wind Energy Science Discussions
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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

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

An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer
journal, January 2019

  • Vali, Mehdi; Petrović, Vlaho; Steinfeld, Gerald
  • Wind Energy Science, Vol. 4, Issue 1
  • DOI: 10.5194/wes-4-139-2019

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm
journal, January 2019

  • Duc, Thomas; Coupiac, Olivier; Girard, Nicolas
  • Wind Energy Science, Vol. 4, Issue 2
  • DOI: 10.5194/wes-4-287-2019

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

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