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Hardware-in-the-Loop Development and Testing of New Pitch Control Algorithms EWEC 2006 Athens

Hardware-in-the-Loop Development and Testing of New Pitch Control Algorithms EWEC 2006 Athens Martin Geyler, Jochen Giebhardt, Bahram Panahandeh Institut für Solare Energieversorgungstechnik (ISET e.V.) Phone: +49-561-7294-364 e-mail: mgeyler@iset.uni-kassel.de. Project Objectives.

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Hardware-in-the-Loop Development and Testing of New Pitch Control Algorithms EWEC 2006 Athens

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  1. Hardware-in-the-Loop Development and Testing of New Pitch Control Algorithms EWEC 2006 Athens Martin Geyler, Jochen Giebhardt, Bahram Panahandeh Institut für Solare Energieversorgungstechnik (ISET e.V.) Phone: +49-561-7294-364 e-mail: mgeyler@iset.uni-kassel.de

  2. Project Objectives Development of advanced pitch control algorithms for load reduction in large wind turbines Project Partners • Individual blade pitch control • compensation for unsymmetrical inflow conditions due to turbulence or deterministic effects • active damping for tower and blades • Modular controller design • Development of safety algorithms • stability monitoring, • handling of sensor faults • Identification of requirements for the pitch system using a Hardware-in-the-Loop test bed setup • dynamics, • loads, wear, • power consumption, thermal losses, • load sensors • communication requirements

  3. Control Problem Schematic of Control Loop

  4. Test Bed: Schematic Overview

  5. Test Bed: Control Concept

  6. Test Bed: Laboratory Setup Controller Rack with Simulation PCs Pitch Drive Inverter Cabinet Load Drive Inverter Cabinet Host PC Load Machines Pitch Motors

  7. Real-Time Simulation: Overall Wind Turbine Model Block structure of Simulink model

  8. Real-Time Simulation: Mechanical Model (1) Multibody approach: • 14 rigid bodies connected by joints: • Universal joints with torsional stiffness and damping representing flexibility of the structure • Revolute joints with external torque input representing actuators • fully recursive algorithm: „Method of Articulated Inertia“: • tree-like structure is exploited • avoids need for inverting large mass matrices • O(N) method: computational effort increases linearly with number of DOF • Mass forces (gravity, inertia) inherently included by the algorithm. • Solver: 3rd-order Runge-Kutta solver at 1ms time step • ca. 450 µs calculation time on Athlon 4000+ PC Mechanical model

  9. Real-Time Simulation: Mechanical Model (2) • Parameters for multi body model were calculated using a optimisation algorithm to find a best fit to a given finite elements (FE) model: • Step: Optimisation of joint locations in order to allow for best representation of first 3 mode shapes • Step: Optimisation of stiffness parameters and joint twist angles in order to fit eigen frequencies and mode shapes • Validation: Comparison of static deflection due to a constant line load (blade) or constant tower top force 1st mode and static deflection of simplified blade model with 2 rigid sections; Comparison with FE model Mechanical model

  10. Real-Time Simulation: Pitch System Model Load torque reference values for load drives will include the following effects: • pitch gear ratio 1:1000 • tooth clearance at fast side of pitch gears • blade bearing friction • DRE/CON-formula for large bearings: • MR = µD/2 * k * M blade root • Four point contact bearings: • µD = 0.006, k = 4.37 • Components for axial and radial force have been neglected. • changing inertia due to blade deflection inherently included by mechanical model Example simulation for pitch load situation in turbulent wind conditions

  11. Real-Time Simulation: Aerodynamic Model (1) • Blade Element Momentum Theory (BEM) • 12 blade elements per blade • semi-empirical corrections: state-of-the-art implementation of • dynamic inflow • yawed inflow • dynamic stall • total 240 aerodynamic states • Solver: simple Forward-Euler integration at 1ms time step • calculation time ca. 45 µs on Athlon 4000+ PC

  12. Real-Time Simulation: Aerodynamic Model (2) • Dynamic Inflow Model (ECN): • Local inflow condition at blade sections depend on free wind speed and load situation of the rotor in a dynamic manner. • Example: Overshoot in blade root bending moment for fast step on pitch angle Tjaereborg Experiment Simulation

  13. Real-Time Simulation: Aerodynamic Model (3) • Dynamic Stall Model (Beddoes-Leishmann-Type): • Effect: dynamic lift forces can be considerable bigger than predicted by stationary cL--curve for fast changes in pitch angle. Simulation Measurement (Risø)

  14. Real-Time Simulation: Turbulent Wind Field Input (1) • 2-D turbulent Wind Field is simulated off-line and read from a file during real time simulation  reproducible time series • 8 x 8 points in the rotor plane, • linear interpolation • only mean wind direction • Method by Mann • wind field is assembled in a 3D-box by means of inverse FFT • Fourier Coefficients calculated from spectral-tensor ( only 11 used ) • „frozen turbulence“ : dimension L1 is used as time axis • Parameter fitting to Kaimal spectrum • Input parameters: • mean wind speed, • mean wind shear, • turbulence intensity • Extreme gust events can be embedded into stochastic turbulent wind field: • Most likely gust shape calculated from correlation matrix R and a given criterion e.g. total jump in wind speed at given location

  15. Real-Time Simulation: Turbulent Wind Field Input (2) Averaged auto-power spectrum for simulated wind fields Example for extreme gust event Criterion: v = 10 m/s, t = 16 s, location

  16. Real-Time Simulation: Visualisation • 3D visualisation tool for motion and load situation of simulated wind turbine • VRML based • Visualisation coupled to real-time simulation via TCP/IP based communication channel Visualisation with VRML

  17. First Results (1) • Algorithm for yaw and tilt moment compensation implemented and tested • (simulated) 1p component in flapwise blade root bending moments is almost cancelled, • pitch drive rating seems sufficient for producing required 1p cyclic pitch offsets, however, considerably increased motion as compared to normal collective pitch operation • simple fuzzy scheme for supervision and controller gain adjustment simulated reduction in 1p component of flapwise blade root bending moment

  18. First Results (2) Measurement of Pitch Drive Load Torques

  19. Conclusions • Hardware-in-the-Loop test bed for Pitch Drives has been developed and successfully taken into operation. • Real-Time Simulation Environment allows for providing realistic load conditions as well as all required feedback signals to the tested Pitch Control System. • First simulation results and measurements for a Yaw- and Tilt-Moment Compensation Controller (Proof-Of-Concept). • It is believed, that the test bed will greatly improve the understanding of the system aspects of advanced pitch control strategies. Thank You.

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