Identification of wind turbine clusters for effective real time yaw control optimization
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Mario A. Rotea | Stefano Leonardi | Umberto Ciri | Federico Bernardoni | M. Rotea | S. Leonardi | U. Ciri | F. Bernardoni
[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 .