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, Simulator fOr Wind Farm Applications (SOWFA), for understanding wake dynamics and an engineering model, FLOw Redirection and Induction in Steady State (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.

[1]  Po-Wen Cheng,et al.  Wake redirecting using feedback control to improve the power output of wind farms , 2016, 2016 American Control Conference (ACC).

[2]  N. Jensen A note on wind generator interaction , 1983 .

[3]  Jennifer Annoni,et al.  Detailed field test of yaw-based wake steering , 2016 .

[4]  Carlo L. Bottasso,et al.  Wind tunnel testing of wake control strategies , 2016, 2016 American Control Conference (ACC).

[5]  Andreas Bechmann,et al.  Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm , 2014 .

[6]  Jennifer Annoni,et al.  Development of FAST.Farm: A New Multiphysics Engineering Tool for Wind Farm Design and Analysis: Preprint , 2017 .

[7]  Stefano Leonardi,et al.  A large-eddy simulation of wind-plant aerodynamics , 2012 .

[8]  Jason R. Marden,et al.  Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study , 2016 .

[9]  Davide Medici,et al.  Experimental studies of wind turbine wakes : power optimisation and meandering , 2005 .

[10]  Martin Kühn,et al.  Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study , 2016 .

[11]  Lucy Y. Pao,et al.  The fatigue loading effects of yaw control for wind plants , 2016, 2016 American Control Conference (ACC).

[12]  L. Machielse,et al.  Controlling Wind in ECN`s Scaled Wind Farm , 2012 .

[13]  J. W. van Wingerden,et al.  Enhanced Kalman Filtering for a 2D CFD NS Wind Farm Flow Model , 2016 .

[14]  Andrew Ning,et al.  Wind plant system engineering through optimization of layout and yaw control , 2016 .

[15]  Martin Kühn,et al.  Full-field assessment of wind turbine near-wake deviation in relation to yaw misalignment , 2016 .

[16]  E. Migoya,et al.  Application of a LES technique to characterize the wake deflection of a wind turbine in yaw , 2009 .

[17]  Andrew Ning,et al.  Maximization of the annual energy production of wind power plants by optimization of layout and yaw‐based wake control , 2017 .

[18]  Kathryn E. Johnson,et al.  Evaluating techniques for redirecting turbine wakes using SOWFA , 2014 .

[19]  Jennifer Annoni,et al.  Full-Scale Field Test of Wake Steering , 2017 .

[20]  Michael Hölling,et al.  Wind tunnel tests on controllable model wind turbines in yaw , 2016 .

[21]  B. M. Doekemeijer,et al.  Enhanced Kalman filtering for a 2D CFD Navier-Stokes wind farm model , 2016 .

[22]  Kathryn E. Johnson,et al.  Simulation comparison of wake mitigation control strategies for a two‐turbine case , 2015 .

[23]  Ewald Krämer,et al.  CFD study on the impact of yawed inflow on loads, power and near wake of a generic wind turbine , 2017 .