Hazim Namik and Karl Stol Department of Mechanical Engineering The University of Auckland

1. Disturbance Accommodating Control of Floating Wind Turbines Hazim Namik and Karl Stol Department of Mechanical Engineering The University of Auckland

2. Outline • Introduction • Individual vs. Collective Blade Pitching • Implemented controllers • Gain Scheduled PI • Periodic LQR • Periodic DAC • Results • Summary 2

3. Introduction • A recent trend in the wind turbine industry is to go offshore • The further offshore the better the wind BUT increased foundation costs • After certain depth, floating wind turbines become feasible 3

4. Floating Wind Turbines Source: Jonkman, J.M., Dynamics Modeling and Loads Analysis of an Offshore Floating Wind Turbine, in Department of Aerospace Engineering Sciences. 2007, University of Colorado: Boulder, Colorado. 4

5. NREL 5MW Wind Turbine • Barge floating platform • 40m×40m×10m • 5MW power rating • 126m diameter rotor (3 Blades) • 90m hub height • Simulated using FAST and Simulink 5

6. Previous Work • Implemented a time-invariant state space controller to address multiple objectives • Power and platform pitch regulation • Performance was improved but... • Conflicting blade pitch commands were issued due to collective blade pitching • Individual blade pitching was proposed

7. Objectives and Scope • Implement individual blade pitching through periodic control • Compare performance of DAC on a floating barge system to previously applied controllers • Disturbance rejection for wind speed changes only • Above rated wind speed region only • Barge platform only

8. Control Options Blade Pitch Generator Torque Collective Pitch Individual Pitch How to Control a Wind Turbine? Source: US Dept. of Energy 8

9. Collective Pitch Restoring Mechanism • Works by changing the symmetric rotor thrust • As turbine pitches • Forward: Rotor thrust is increased • Backward: Rotor thrust is reduced • Pitching conflicts with speed regulation 9

10. Individual Pitch Restoring Mechanism • Works by creating asymmetric thrust loads • As turbine pitches • Forward: • Blades at the top increase thrust • Blades at the bottom reduce thrust • Backward: vice versa 10

11. Controllers Implemented Gain Scheduled PI (GSPI) Periodic Linear Quadratic Regulator (PLQR) Periodic Disturbance Accommodating Controller (PDAC) 11

12. Baseline Controller • Generator torque controller • Regulate power above rated • Collective pitch controller • Regulate generator speed above rated wind speed • Gain scheduled PI controller

13. Periodic gain matrices States vector Actuators vector State Space Control • Requires a linearized state space model • Control law (requires a state estimator)

14. Generic Block Diagram

15. Periodic LQR • Periodic gains result in individual blade pitching • Requires 5 degrees of freedom (DOFs) model to ensure stability • Platform Roll and Pitch • Tower 1st side-side bending mode • Generator and Drivetrain twist • Part of DAC: State regulation

16. Disturbance Accommodating Control • Time variant state space model with disturbances • Disturbance waveform model

17. Disturbance Accommodating Control (Cont.) • Form the DAC law (requires disturbance estimator) • New state equation becomes • To minimize effect of disturbances

18. Controllers Comparison SISO: Single-Input Single-Output MIMO: Multi-Input Multi-Output

19. 1 DOF DAC Simulation Result

20. Full DOFs Simulation Result Power and Speed Fatigue Loads Platform Motions

21. Reasons for Poor Performance • High Gd gain causing extensive actuator saturation • System nonlinearities and un-modeled DOFs • System may not be stable in the nonlinearmodel

22. Effect of Adding Platform Yaw Power and Speed Fatigue Loads Platform Motions

23. Conclusions • The periodic LQR significantly improved performance since it utilises individual blade pitching • Adding DAC gave mixed performance due to actuator saturation • DAC for the wind fluctuations may not be the ideal controller for a floating barge concept 23

24. Future Work • Variable pitch operating point • Follow optimum operating point • DAC for waves • Effect on Bd Matrix • Simple moment disturbance

25. Thank You

26. Offshore Wind Turbines • Why go offshore? • Better wind conditions • Stronger and steadier • Less turbulent • Can be located close to major demand centres • Operate at maximum efficiency (e.g. no noise regulations) • Increased foundation costs with increasing water depth 26

27. Going Further Offshore Land-Based Shallow Water Transitional Depth Deepwater Floating Water Depth: 0 – 30 m 30 – 50 m 50 – 200 m Source: Jonkman, J.M., Dynamics Modeling and Loads Analysis of an Offshore Floating Wind Turbine, in Department of Aerospace Engineering Sciences. 2007, University of Colorado: Boulder, Colorado. 27

28. FAST Simulation Tool • Fatigue, Aerodynamics, Structures and Turbulence Source: Jonkman, J.M., Dynamics Modeling and Loads Analysis of an Offshore Floating Wind Turbine, in Department of Aerospace Engineering Sciences. 2007, University of Colorado: Boulder, Colorado. 28

29. Wind and Wave 29

30. Power Regions • Region 1 • No power is generated below the cut in speed • Region 2 • Maximise power capture • Region 3 • Regulate to the rated power 30

31. Torque Controller • Region 1 • Region 2 • Region 3 • Regions 1.5 and 2.5 are linear transitions between the regions 31

32. Region 2.5 Torque Controller Region 1.0 Region 1.5 Region 2.0 Region 3.0 32

33. Collective Pitch Controller • PI Controller to regulate generator speed • Controller gains calculated according to the design parameters • ωn = 0.7 rad/s and ζ = 0.7 • Simple DOF model with PI controller gives 33

34. Gain Scheduled PI Gains 34

35. Riccati Equations • Optimal gain and Algebraic Riccati Equation • Optimal periodic gain and Periodic Riccati Equation 35

36. FAST Aero-hydro-servo-elastic simulator Nonlinear equations of motion Can be linked to Simulink Find linearized state-space model for controller design MATLAB/Simulink Design controllers using linear control theory Easy graphical implementation Powerful design tools to help design controllers Flexible Simulation Tools 36

37. Periodic Gains • Changes with rotor azimuth • Same for each blade but ±120° out of phase • Gain for state 3 changes sign when blade is at lower half of rotor 37