Individual Blade Pitch Control Design for Load Reduction on Large Wind Turbines EWEC 2007

1. Individual Blade Pitch Control Design for Load Reduction on Large Wind Turbines EWEC 2007 Milano, 7-10 May 2007 Martin Geyler, Peter Caselitz Institut für Solare Energieversorgungstechnik (ISET e.V.) Telefon: +49-561-7294-364 e-mail: mgeyler@iset.uni-kassel.de

2. Tasks of Pitch Control (1) • Basic Pitch Control Objectives: • Rotor speed control, • Limitation of power capture at high wind speeds  Collective Pitch • Safety System: • Redundant aerodynamic brakes Individual Pitch

3. Tasks of Pitch Control (2) Additional Objectives for Fatigue Load Reduction: • Active damping of 1st axial tower bending mode •  Collective Pitch • Suppression of 1p fluctuations in flapwise blade bending stress, • Compensation of yaw and tilt moments on nacelle • (due to yaw misalignment, • wind shear, turbulence) •  Individual Pitch

4. Control Design Objectives (Full Load Operation) • At the presence of fluctuating aerodynamic forces acting on the rotor blades (due to turbulence, yaw misalignment etc.) the controller should act to: • Minimize deviation of rotor speed from rated speed • Minimize tower top acceleration in the range of the first tower bending eigen frequency • Minimize 1p component in flapwise blade root bending moments (yaw and tilt moment compensation)  Disturbance rejection problem

5. Control Design Limiting Conditions • Restrictions in pitch speed / acceleration • Rating of pitch drives, transmissions • Loading of blades • Avoid harmful interaction of pitch control with structural modes • Due to coupling between axial / tangential aerodynamic forces • speed control  1st axial tower bending mode • active tower damping  synchronous flapwise blade bending modes • yaw/tilt moment compensation  asynchronous flapwise blade bending modes • Robustness issues • Limited accuracy of model used for control design • Uncertainty / changes in aerodynamic coefficients over operating range •  Bandwidth limitations for pitch control

6. Modular Control Design • Transparent structure w.r.t • parameter tuning, • output limitation, • All controllers act on pitch angles demand •  strong coupling esp. between tower damping and speed control, • “One loop at a time” design approach: interactions between individual control loops may cause problems.

7. Multivariable Control Design • General • Based on plant model to account for couplings between multiple inputs/outputs • All control loops are designed simultaneously. • Weighted optimisation criterion to account for several control objectives • H-Norm Minimisation Approach • Frequencies of disturbances are known: 1p, fTower, fBlade. • Control objectives are conveniently formulated in the frequency domain by means of weighting functions. • Robustness requirements can be easily included into controller specification.

8. Linear Model for Multivariable Control Design MV control design requires simple, linear model of wind turbine including the relevant effects: • Linearised Aerodynamics • changes in • blade total thrust force, • blade flapwise aerodynamic moment, • blade edgewise aerodynamic moment, • depending on changes in effective wind speed, rotor speed and pitch angle, • Structural dynamics • turbine inertia • axial tower bending • flapwise blade bending Simplified model for coupled axial oscillations of tower top and blades

9. Parameter Identification • Estimation of non-physical parameters of simplified structural dynamics modelpossible from measured / simulated time series of • tower top acceleration, • blade root bending moments, • using LS methods. • Defined excitation • pitch angle changes, • snap-back cable • Disturbances • turbulence influence, • numerical drift effects, • can be minimised by appropriate filtering. Parameter identification from simulated time series at 10% turbulence intensity

10. Integral MV Controller Structure • high order, low transparency of controller • reference values for indidividual blade pitch angles not divided into collective / cyclic components, which is desirable for limitation of pitch angle deviations and supervision • full load / part load transition requires switching of controllers

11. Decoupled MV Controller Structure Simplified turbine model can be divided into collective pitch / cyclic pitch models using a transformation  decoupled controller design Collective Pitch Controller: speed control, active tower damping Cyclic Pitch Controller: yaw and tilt moment compensation

12. Collective Pitch Control Design (1) Block scheme for collective pitch control design

13. Collective Pitch Control Design (2) Speed Control Active damping of axial tower oscillations Influence on 1st synchronous blade bending mode

14. Collective Pitch Control Design (3) Use of pitch angle weighting function Wp,0 • Account for limits in pitch speed / pitch acceleration by limiting controller bandwidth 2. Ensure sufficient robustness against modelling uncertainty at higher frequencies max. singular value for nominal plant (blue) robust stability limit for max. singular value of additive perturbations (red) pitch angle weighting function (black)

15. Collective Pitch Control Design (4) Analysis of robustness against changes in aerodynamic coefficients for operational range 12 m/s < vWind < 24 m/s

16. Cyclic Pitch Control Design (1) Block scheme for cyclic pitch control design

17. Cyclic Pitch Control Design (2) Open loop and closed loop transfer functions in the transformed system from disturbance (aerodynamic) yaw/tilt moment to measured yaw/tilt moment (derived from blade root bending moments) H controller (red) PI controller (green)

18. Cyclic Pitch Control Design (3) • Robustness to • variation in aerodynamic coefficients (low frequencies) • modelling uncertainty (high frequencies) max. singular values for additive perturbations of nominal plant (blue) 12 m/s < vWind < 24 m/s • robust stability limits for • H controller (red) • PI controller (green)

19. Nonlinear Simulation (1) Detailed model of the wind turbine • Aerodynamics: • blade element method, • dynamic inflow model, • dynamic stall model, • aerodynamic damping • Structural dynamics: • Multi body model in 3D space, • yaw / tilt movement of nacelle, • oscillation direction of blades depending on pitch angle, • centrifugal stiffening • Pitch System: • detailed actuator model, • pitch gear teeth clearance, • blade bearing friction , • blade inertia around pitch axis depending on blade bending multi body model for description of wind turbine structural dynamics

20. Nonlinear Simulation (2) Comparison of time series for baseline controller (blue ) / MV controller (red) • Step on wind speed vWind = +1 m/s vWind,0 = 16 m/s • Step on wind direction Wind = 15° tower top acceleration flapwise blade root bending moment blade 1 rotor speed pitch angle blade 1

21. Nonlinear Simulation (3) Comparison of time series for baseline controller (blue ) / MV controller (red) • Step on wind speed vWind = +1 m/s vWind,0 = 12 m/s • Step on wind direction Wind = 15° tower top acceleration flapwise blade root bending moment blade 1 rotor speed pitch angle blade 1

22. Nonlinear Simulation (4) Comparison of time series for baseline controller (blue ) / MV controller (red) • Step on wind speed vWind = +1 m/s vWind,0 = 20 m/s • Step on wind direction Wind = 15° tower top acceleration flapwise blade root bending moment blade 1 rotor speed pitch angle blade 1

23. Hardware-in-the-Loop Test Bed

24. Results Hardware-in-the-LoopTest Bed (1) Comparison of time series for baseline controller (blue ) / MV controller (red) (Mean wind speed 15 m/s, Yaw misalignment 15°, Turbulence intensity 10%) rotor speed generator power pitch angle blade 1 pitch actuator torque blade 1 pitch actuator power blade 1

25. Results Hardware-in-the-Loop Testbed (2) Comparison of amplitude spectra for baseline controller (blue ) / MV controller (red) (Mean wind speed 15 m/s, Yaw misalignment 15°, Turbulence intensity 10%) yaw / tilt moment compensation flapwise blade root bending moment blade 1 axial tower top acceleration pitch actuator torque blade 1 active tower damping

26. Summary / Outlook • Summary • The pitch control problem with additional load reduction objectives is multivariable by nature. • Control design approach based on H-Norm-Minimisation has been discussed, based on a simple linear model of the wind turbine. • Decoupled controllers can be designed for collective pitch (speed control, active tower damping) and cyclic pitch (yaw and tilt moment compensation). • The controllers show sufficient robustness to cover the entire full load operating region; robustness to modelling uncertainty can be easily adressed in the design approach. • Outlook • Investigate performance limits of speed control. • Investigate gain scheduling.