Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2

This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in Fleming et al. (2019). The authors implemented wake steering on two turbine pairs, and compared results with the latest FLORIS (FLOw Redirection and Induction in Steady State) model of wake steering, showing good agreement in overall energy increase. Further, although not the original intention of the study, we also used the results to detect the secondary steering phenomenon. Results show an overall reduction in wake losses of approximately 6.6 % for the regions of operation, which corresponds to achieving roughly half of the static optimal result. Copyright statement. This work was authored by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under contract no. DE-AC36-08GO28308. Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

[1]  Jennifer Annoni,et al.  Optimization Under Uncertainty for Wake Steering Strategies , 2017 .

[2]  J. Lundquist,et al.  How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine , 2019, Wind Energy Science.

[3]  Ryan N. King,et al.  Controls-Oriented Model for Secondary Effects of Wake Steering , 2020 .

[4]  Fernando Porté-Agel,et al.  Wind farm power optimization via yaw angle control: A wind tunnel study , 2019, Journal of Renewable and Sustainable Energy.

[5]  P. Fleming,et al.  Design and analysis of a wake steering controller with wind direction variability , 2019, Wind Energy Science.

[6]  Jan-Willem van Wingerden,et al.  Feedforward-Feedback wake redirection for wind farm control , 2019 .

[7]  Ervin Bossanyi Combining induction control and wake steering for wind farm energy and fatigue loads optimisation , 2018 .

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

[9]  Jennifer Annoni,et al.  Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model , 2016 .

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

[11]  Johan Meyers,et al.  Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions , 2016 .

[12]  Jan-Willem van Wingerden,et al.  Ensemble Kalman filtering for wind field estimation in wind farms , 2017, 2017 American Control Conference (ACC).

[13]  T. Ishihara,et al.  A new Gaussian-based analytical wake model for wind turbines considering ambient turbulence intensities and thrust coefficient effects , 2018, Journal of Wind Engineering and Industrial Aerodynamics.

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

[15]  E. Bossanyi,et al.  Engineering models for turbine wake velocity deficit and wake deflection. A new proposed approach for onshore and offshore applications , 2019, Journal of Physics: Conference Series.

[16]  Paul Fleming,et al.  A simulation study demonstrating the importance of large-scale trailing vortices in wake steering , 2018 .

[17]  F. Blondel,et al.  An alternative form of the super-Gaussian wind turbine wake model , 2020 .

[18]  Brady Ryan,et al.  Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1 , 2019, Wind Energy Science.

[19]  Thomas Bak,et al.  Survey of wind farm control - power and fatigue optimization , 2015 .

[20]  F. Porté-Agel,et al.  Experimental and theoretical study of wind turbine wakes in yawed conditions , 2016, Journal of Fluid Mechanics.

[21]  Fernando Porté-Agel,et al.  A new analytical model for wind farm power prediction , 2015 .

[22]  Jan-Willem van Wingerden,et al.  Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control , 2019 .

[23]  P. Fleming,et al.  The aerodynamics of the curled wake: a simplified model in view of flow control , 2018, Wind Energy Science.

[24]  P. Fleming,et al.  Field Validation of Wake Steering Control with Wind Direction Variability , 2020, Journal of Physics: Conference Series.

[25]  Kathryn E. Johnson,et al.  Wind direction estimation using SCADA data with consensus-based optimization , 2019 .

[26]  Jonathan White,et al.  Estimation of Rotor Loads Due to Wake Steering , 2018 .

[27]  Sanjiva K. Lele,et al.  Wind farm power optimization through wake steering , 2019, Proceedings of the National Academy of Sciences.

[28]  Martin Kühn,et al.  Robust active wake control in consideration of wind direction variability and uncertainty , 2018, Wind Energy Science.

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

[31]  Jennifer Annoni,et al.  Assessment of wind turbine component loads under yaw-offset conditions , 2017 .

[33]  F. Blondel,et al.  An alternative form of the super-Gaussian wind turbine wake model , 2020, Wind Energy Science.

[34]  F. Porté-Agel,et al.  A new analytical model for wind-turbine wakes , 2013 .

[35]  Morten Hartvig Hansen,et al.  Load alleviation of wind turbines by yaw misalignment , 2014 .