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Disturbance Accommodating Control of Floating Wind Turbines
Hazim Namik and Karl Stol Department of Mechanical Engineering The University of Auckland
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Outline Introduction Individual vs. Collective Blade Pitching
Implemented controllers Gain Scheduled PI Periodic LQR Periodic DAC Results Summary 2
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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
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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
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NREL 5MW Wind Turbine Barge floating platform 5MW power rating
40m×40m×10m 5MW power rating 126m diameter rotor (3 Blades) 90m hub height Simulated using FAST and Simulink 5
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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
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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
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How to Control a Wind Turbine?
Control Options Blade Pitch Generator Torque Collective Pitch Individual Pitch Source: US Dept. of Energy 8
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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
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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
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Controllers Implemented
Gain Scheduled PI (GSPI) Periodic Linear Quadratic Regulator (PLQR) Periodic Disturbance Accommodating Controller (PDAC) 11
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Baseline Controller Generator torque controller
Regulate power above rated Collective pitch controller Regulate generator speed above rated wind speed Gain scheduled PI controller
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State Space Control Requires a linearized state space model
Periodic gain matrices States vector Actuators vector Control law (requires a state estimator)
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Generic Block Diagram
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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
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Disturbance Accommodating Control
Time variant state space model with disturbances Disturbance waveform model
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Disturbance Accommodating Control (Cont.)
Form the DAC law (requires disturbance estimator) New state equation becomes To minimize effect of disturbances
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Controllers Comparison
GSPI PLQR Gains Calculation Gain scheduled Periodic Riccati Equation Blade Pitching Collective Individual Pros Simple and robust MIMO Multi-objective Individual Pitching Cons SISO Single-objective Collective pitching Complicated GSPI Gains Calculation Gain scheduled Blade Pitching Collective Pros Simple and robust Cons SISO Single-objective Collective pitching GSPI PLQR PDAC Gains Calculation Gain scheduled Periodic Riccati Equation Periodic Riccati Equation + DAC Blade Pitching Collective Individual Pros Simple and robust MIMO Multi-objective Individual Pitching All PLQR pros + Disturbance Rejection Cons SISO Single-objective Collective pitching Complicated Most complicated Requires a dist. estimator SISO: Single-Input Single-Output MIMO: Multi-Input Multi-Output
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1 DOF DAC Simulation Result
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Full DOFs Simulation Result
Power and Speed Fatigue Loads Platform Motions
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Reasons for Poor Performance
High Gd gain causing extensive actuator saturation System nonlinearities and un-modeled DOFs System may not be stable in the nonlinear model
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Effect of Adding Platform Yaw
Power and Speed Fatigue Loads Platform Motions
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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
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Future Work Variable pitch operating point DAC for waves
Follow optimum operating point DAC for waves Effect on Bd Matrix Simple moment disturbance
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Thank You
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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
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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
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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
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Wind and Wave 29
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Power Regions Region 1 Region 2 Region 3
No power is generated below the cut in speed Region 2 Maximise power capture Region 3 Regulate to the rated power 30
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Torque Controller Region 1 Region 2 Region 3
Regions 1.5 and 2.5 are linear transitions between the regions 31
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Torque Controller Region 2.5 Region 1.0 Region 1.5 Region 2.0
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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
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Gain Scheduled PI Gains
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Riccati Equations Optimal gain and Algebraic Riccati Equation
Optimal periodic gain and Periodic Riccati Equation 35
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Simulation Tools FAST MATLAB/Simulink
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 36
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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
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