欧洲风能技术论文68_Hardware-in-the-Loop_Development_and_Testing_of_New.ppt

,Hardware-in-the-Loop Development and Testing of New Pitch Control Algorithms EWEC 2006 Athens Martin Geyler, Jochen Giebhardt, Bahram Panahandeh Institut fr Solare Energieversorgungstechnik (ISET e.V.) Phone: +49-561-7294-364 e-mail: mgeyleriset.uni-kassel.de,Project Objectives,Individual blade pitch control compensation for unsymmetrical inflow conditions due to turbulence or deterministic effects active damping for tower and blades,Project Partners,Development 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,Control Problem,Schematic of Control Loop,Test Bed: Schematic Overview,Test Bed: Control Concept,Test Bed: Laboratory Setup,Load Drive Inverter Cabinet,Pitch Drive Inverter Cabinet,Pitch Motors,Load Machines,Controller Rack with Simulation PCs,Host PC,Block structure of Simulink model,Real-Time Simulation: Overall Wind Turbine Model,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,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 s calculation time on Athlon 4000+ PC,Multibody approach:,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,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,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 = D/2 * k * M blade root Four point contact bearings: D = 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,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 s on Athlon 4000+ PC,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),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 (Ris),Real-Time Simulation: Aerodynamic Model (3),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 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,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,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,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 c