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

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

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

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  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