Identification of wind turbine clusters for effective real time yaw control optimization

Control algorithms seeking to maximize wind plant power production may not require that all turbines communicate with each other for the purpose of coordinating an optimal control solution. In practice, an efficient and robust control solution may result by coordinating only turbines that are aerodynamically coupled through wake effects. The implementation of such control strategy would require information of which clusters of turbines are coupled in this way. As the wind changes direction, the clusters of coupled turbines may vary continuously within the array. Hence, in practical applications, the identification of these clusters has to be performed in real time in order to efficiently apply a coordinated control approach. Results from large eddy simulations of the flow over a wind farm array of 4 × 4 turbines are used to mimic Supervisory Control And Data Acquisition (SCADA) data needed for the cluster identification method and to evaluate the effectiveness of the yaw control applied to the identified clusters. Results show that our proposed method is effective in identifying turbine clusters, and that their optimization leads to a significant gain over the baseline. When the proposed method does not find clusters, the yaw optimization is ineffective in increasing the power of the array of turbines. This study provides a model-free method to select the turbines that should communicate with another to increase power production in real time. In addition, the analysis of the flow field provides general insights on the effect of the local induction, as well as of the wind farm blockage, on yaw optimization strategies.

[1]  Paul Fleming,et al.  Field test of wake steering at an offshore wind farm , 2017 .

[2]  P. Fleming,et al.  Evaluation of the potential for wake steering for U.S. land-based wind power plants , 2021, Journal of Renewable and Sustainable Energy.

[3]  Jan-Åke Dahlberg,et al.  Blockage effects in wind farms , 2020 .

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

[5]  Mario A. Rotea,et al.  Logarithmic Power Feedback for Extremum Seeking Control of Wind Turbines , 2017 .

[6]  E. S. Politis,et al.  Modelling and Measuring Flow and Wind Turbine Wakes in Large Wind Farms Offshore , 2009, Renewable Energy.

[7]  Paolo Orlandi,et al.  DNS of turbulent channel flows with two- and three-dimensional roughness , 2006 .

[8]  Stefano Leonardi,et al.  Large-Eddy Simulations of Two In-Line Turbines in a Wind Tunnel with Different Inflow Conditions , 2017 .

[9]  Brady Ryan,et al.  Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 , 2020, Wind Energy Science.

[10]  Filippo Campagnolo,et al.  Wind tunnel testing of a closed-loop wake deflection controller for wind farm power maximization , 2016 .

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

[12]  Fernando Porté-Agel,et al.  A wind-tunnel investigation of wind-turbine wakes in yawed conditions , 2015 .

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

[15]  Renzo Ruisi,et al.  Wind Farm Blockage and the Consequences of Neglecting Its Impact on Energy Production , 2018, Energies.

[16]  M. Rotea,et al.  Real-time identification of clusters of turbines , 2020, Journal of Physics: Conference Series.

[17]  Charles Meneveau,et al.  Temporal structure of aggregate power fluctuations in large-eddy simulations of extended wind-farms , 2014, 1412.7238.

[18]  L.Y. Pao,et al.  Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture , 2006, IEEE Control Systems.

[19]  Jizhen Liu,et al.  A dynamic clustering model of wind farm based on the operation data , 2015 .

[20]  Wenhui Shi,et al.  Dynamic clustering equivalent model of wind turbines based on spanning tree , 2015 .

[21]  R. B. Cal,et al.  Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines , 2021, Journal of Fluid Mechanics.

[22]  B. Ganapathisubramani,et al.  The effects of free-stream turbulence on the performance of a model wind turbine , 2021, Journal of Renewable and Sustainable Energy.

[23]  M. Calaf,et al.  Classification of the Reynolds stress anisotropy tensor in very large thermally stratified wind farms using colormap image segmentation , 2019, Journal of Renewable and Sustainable Energy.

[24]  Charles Meneveau,et al.  Modelling yawed wind turbine wakes: a lifting line approach , 2018, Journal of Fluid Mechanics.

[25]  S. Leonardi,et al.  One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data , 2020 .

[26]  On the interaction of very-large-scale motions in a neutral atmospheric boundary layer with a row of wind turbines , 2018, Journal of Fluid Mechanics.

[27]  Varun Sharma,et al.  Investigation of the incoming wind vector for improved wind turbine yaw-adjustment under different atmospheric and wind farm conditions , 2017 .

[28]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .

[29]  Jens Nørkær Sørensen,et al.  Analysis of Power Enhancement for a Row of Wind Turbines Using the Actuator Line Technique , 2007 .

[30]  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.

[31]  Leo E. Jensen,et al.  The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm , 2010 .

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

[33]  Stefano Leonardi,et al.  Channel flow over large cube roughness: a direct numerical simulation study , 2010, Journal of Fluid Mechanics.

[34]  I. Orlanski A Simple Boundary Condition for Unbounded Hyperbolic Flows , 1976 .

[35]  Yongqian Liu,et al.  Clustering methods of wind turbines and its application in short-term wind power forecasts , 2014 .

[36]  Bart De Schutter,et al.  A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach , 2020 .

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

[38]  R. B. Cal,et al.  Data-driven modeling of the wake behind a wind turbine array , 2020 .

[39]  Lars Sætran,et al.  Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines , 2018, Wind Energy Science.

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

[41]  Stefano Leonardi,et al.  Effect of tower and nacelle on the flow past a wind turbine , 2017 .

[42]  Eunkuk Son,et al.  Blade pitch angle control for aerodynamic performance optimization of a wind farm , 2013 .

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

[44]  Johan Meyers,et al.  Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization , 2018 .

[45]  M. Rotea,et al.  Effect of the turbine scale on yaw control , 2018, Wind Energy.

[46]  Johan Meyers,et al.  Optimal turbine spacing in fully developed wind farm boundary layers , 2012 .

[47]  Johan Meyers,et al.  Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel , 2016 .

[48]  J. Peinke,et al.  Wind turbine wake intermittency dependence on turbulence intensity and pitch motion , 2019, Journal of Renewable and Sustainable Energy.

[49]  J. Dabiri,et al.  Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions , 2020 .

[50]  T. F. Pedersen,et al.  On wind turbine power performance measurements at inclined airflow , 2004 .

[51]  Marc Calaf,et al.  Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes , 2021 .

[52]  Aaron D. Smith,et al.  Wind Plant Preconstruction Energy Estimates. Current Practice and Opportunities , 2016 .

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

[54]  Paul Fleming,et al.  Incorporating Atmospheric Stability Effects into the FLORIS Engineering Model of Wakes in Wind Farms , 2016 .

[55]  Wake position tracking using dynamic wake meandering model and rotor loads , 2021 .

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

[57]  Mario A. Rotea,et al.  Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking , 2017 .

[58]  U Ciri,et al.  Increasing wind farm efficiency by yaw control: beyond ideal studies towards a realistic assessment , 2020, Journal of Physics: Conference Series.

[59]  Muyiwa S. Adaramola,et al.  Experimental investigation of wake effects on wind turbine performance , 2011 .

[60]  M. Rotea,et al.  Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations , 2019, Wind Energy.

[61]  Charles Meneveau,et al.  Modeling space-time correlations of velocity fluctuations in wind farms , 2017, 1710.01659.

[62]  Michael Sinner,et al.  Experimental results of wake steering using fixed angles , 2021, Wind Energy Science.

[63]  Yu Huang,et al.  Improved clustering and deep learning based short-term wind energy forecasting in large-scale wind farms , 2020 .

[64]  Mario A. Rotea,et al.  Dynamic Programming Framework for Wind Power Maximization , 2014 .

[65]  E. Simley,et al.  Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings , 2021 .

[66]  Jie Zhang,et al.  A clustering-based scenario generation framework for power market simulation with wind integration , 2020 .