Efficient stochastic wake modeling for wind farm control

Recent research into wind farm control promises the ability to create more densely populated wind farms and improved power production of existing wind farms by controlling for wake interactions between turbines. In this paper, a computationally efficient Discrete time Stochastic and Dynamic model (DStoDyn) is developed using of a wide sense stationary random variable with deterministic mean to model wind direction uncertainties in a wind farm. Comparisons to results obtained from the large eddy simulation software SOWFA show that general wake displacement trends may be represented by DStoDyn.

[1]  J. Meyers,et al.  Optimal control of energy extraction in wind-farm boundary layers , 2015, Journal of Fluid Mechanics.

[2]  John Lygeros,et al.  Model based power optimisation of wind farms , 2014, 2014 European Control Conference (ECC).

[3]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[4]  Johan Meyers,et al.  Analysis of turbulent flow properties and energy fluxes in optimally controlled wind-farm boundary layers , 2014 .

[5]  Kincho H. Law,et al.  Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization , 2016, IEEE Transactions on Control Systems Technology.

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

[7]  Jan-Willem van Wingerden,et al.  SOWFA Super-Controller: A High-Fidelity Tool for Evaluating Wind Plant Control Approaches , 2013 .

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

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

[10]  Kathryn E. Johnson,et al.  Assessment of Extremum Seeking Control for Wind Farm Energy Production , 2012 .

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

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[13]  Matthew J. Churchfield,et al.  Wind Turbine Wake-Redirection Control at the Fishermen's Atlantic City Windfarm: Preprint , 2015 .

[14]  Eilyan Bitar,et al.  Coordinated control of a wind turbine array for power maximization , 2013, 2013 American Control Conference.

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

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

[17]  Yaoyu Li,et al.  Optimizing Energy Capture of Cascaded Wind Turbine Array With Nested-Loop Extremum Seeking Control , 2015 .

[18]  Z. Q. Bo,et al.  A wind farm coordinated controller for power optimization , 2011, 2011 IEEE Power and Energy Society General Meeting.

[19]  Skipper Seabold,et al.  Time Series Analysis in Python with statsmodels , 2011, SciPy.

[20]  J. W. van Wingerden,et al.  A predictive control framework for optimal energy extraction of wind farms , 2016 .

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

[22]  Martin Kühn,et al.  Quasi Linear Parameter Varying modeling for wind farm control using the 2D Navier-Stokes equations , 2016, 2016 American Control Conference (ACC).

[23]  Kincho H. Law,et al.  A data-driven approach for cooperative wind farm control , 2016, 2016 American Control Conference (ACC).

[24]  P.M.O. Gebraad.  Data-driven wind plant control , 2014 .