EWEC 2006, AthensMartin Geyler 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:

EWEC 2006, AthensMartin Geyler 2 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

EWEC 2006, AthensMartin Geyler 3 Control Problem Schematic of Control Loop

EWEC 2006, AthensMartin Geyler 4 Test Bed: Schematic Overview

EWEC 2006, AthensMartin Geyler 5 Test Bed: Control Concept

EWEC 2006, AthensMartin Geyler 6 Test Bed: Laboratory Setup Load Drive Inverter Cabinet Pitch Drive Inverter Cabinet Pitch Motors Load Machines Controller Rack with Simulation PCs Host PC

EWEC 2006, AthensMartin Geyler 7 Block structure of Simulink model Real-Time Simulation: Overall Wind Turbine Model

EWEC 2006, AthensMartin Geyler 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: 3 rd -order Runge-Kutta solver at 1ms time step -ca. 450 µs calculation time on Athlon PC Multibody approach:

EWEC 2006, AthensMartin Geyler 9 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 shapes 2.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

EWEC 2006, AthensMartin Geyler 10 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: M R = µ D /2 * k * M blade root -Four point contact bearings: µ D = 0.006, k = Components for axial and radial force have been neglected. -changing inertia due to blade deflection inherently included by mechanical model

EWEC 2006, AthensMartin Geyler 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 PC

EWEC 2006, AthensMartin Geyler 12 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)

EWEC 2006, AthensMartin Geyler 13 Dynamic Stall Model (Beddoes-Leishmann-Type): -Effect: dynamic lift forces can be considerable bigger than predicted by stationary c L -  -curve for fast changes in pitch angle. Simulation Measurement (Risø) Real-Time Simulation: Aerodynamic Model (3)

EWEC 2006, AthensMartin Geyler 14 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

EWEC 2006, AthensMartin Geyler 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

EWEC 2006, AthensMartin Geyler 16 -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

EWEC 2006, AthensMartin Geyler 17 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

EWEC 2006, AthensMartin Geyler 18 First Results (2) Measurement of Pitch Drive Load Torques

EWEC 2006, AthensMartin Geyler 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.