EWEC 2006, Athens Martin Geyler
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Hardware-in-the-Loop Development and Testing of New Pitch Control Algorithms
EWEC 2006 Athens
Martin Geyler, Jochen Giebhardt, Bahram PanahandehInstitut für Solare Energieversorgungstechnik (ISET e.V.)Phone: +49-561-7294-364e-mail: [email protected]
EWEC 2006, Athens Martin Geyler
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Project Objectives
- Individual blade pitch control- compensation for unsymmetrical
inflow conditions due to turbulence or deterministic effects
- active damping for tower and blades
Project PartnersDevelopment of advanced pitch control algorithms for load reduction in large wind turbines
- 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
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Control Problem
Schematic of Control Loop
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Test Bed: Schematic Overview
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Test Bed: Control Concept
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Test Bed: Laboratory Setup
Load DriveInverter Cabinet
Pitch DriveInverter Cabinet
Pitch Motors
Load Machines
Controller Rack with Simulation PCs
Host PC
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Block structure of Simulink model
Real-Time Simulation: Overall Wind Turbine Model
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- 14 rigid bodies connected by joints:(1) Universal joints with torsional stiffness
and damping representing flexibility of the structure
(2) Revolute joints with external torque input representing actuators
Mechanical model
Real-Time Simulation: Mechanical Model (1)
- 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
Multibody approach:
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Mechanical model
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:
1st mode and static deflection of simplified blade model with 2 rigid sections;Comparison with FE model
1. Step: Optimisation of joint locations in order to allow for best representation of first 3 mode shapes2. 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
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Load torque reference values for load drives will include the following effects:
Example simulation for pitch load situation in turbulent wind conditions
Real-Time Simulation: Pitch System Model
- 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
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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 stepcalculation time ca. 45 µs on Athlon 4000+ PC
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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
Simulation Tjaereborg Experiment
Real-Time Simulation: Aerodynamic Model (2)
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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)
Real-Time Simulation: Aerodynamic Model (3)
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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
Real-Time Simulation: Turbulent Wind Field Input (1)
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 axisParameter 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
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Real-Time Simulation: Turbulent Wind Field Input (2)
Averaged auto-power spectrum for simulated wind fields
Example for extreme gust eventCriterion: v = 10 m/s, t = 16 s, location
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- 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
Real-Time Simulation: Visualisation
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First Results (1)
- Algorithm for yaw and tilt moment compensation implemented and tested
simulated reduction in 1p component of flapwise blade root bending moment
- (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
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First Results (2)
Measurement of Pitch Drive Load Torques
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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.