INTEGRATED ACTIVE AND PASSIVE LOAD MITIGATION IN WIND TURBINES. C.L. Bottasso, F. Campagnolo, A. Croce, C. Tibaldi Politecnico di Milano, Italy

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1 di MI tecnico POLI tecnico lano INTEGRATED ACTIVE AND PASSIVE LOAD MITIGATION IN WIND TURBINES C.L. Bottasso, F. Campagnolo, A. Croce, C. Tibaldi Politecnico di Milano, Italy Wind Turbine Control Symposium November 2011, Ålborg, Denmark

cost of energy: Reduce extreme loads Reduce fatigue

2 Need for Load Mitigation Trends in wind energy: Increasing wind turbine size Off-shore wind To decrease cost of energy: Reduce extreme loads Reduce fatigue damage Limit actuator duty cycle Ensure high reliability/availability

3 Presentation Outline

Active Load Mitigation: Pitch Control Individual blade Pitch Control (IPC) Inner loop (collective pitch): regulation to set point and alleviation of gust loads Outer loops (individual pitch):

4 Active Load Mitigation: Pitch Control Individual blade Pitch Control (IPC) Inner loop (collective pitch): regulation to set point and alleviation of gust loads Outer loops (individual pitch): reduction of - Deterministic (periodic) loads due to blade weight and non-uniform inflow - Non-deterministic loads, caused by fast temporal and small spatial turbulent wind fluctuations Uniform wind Turbulent wind

reduction of - Deterministic (periodic) loads due to blade weight and non-uniform inflow -

5 Active Load Mitigation: Predictive LiDAR-Enabled Pitch Control LiDAR: generic model, captures realistically wind filtering due to volumetric averaging Receding Horizon Control: model predictive formulation with wind scheduled linear model, real-time implementation based on CVXGEN Non-Homogeneous LQR Control: approximation of RHC, extremely low computational cost LiDAR prediction span Reduced peak values Reduced peak values Reduced peak to peak oscillations

linear model, real-time implementation based on CVXGEN Non-Homogeneous LQR Control: approximation of RHC,

6 Active Load Mitigation: Distributed Control Flow control devices: TE flaps Microtabs Vortex generators Active jets (plasma, synthetic) Morphing airfoils (Credits: Risoe DTU) (Credits: Risoe DTU) (Chow and van Dam 2007) (Credits: Smart Blade GmbH)

(plasma, synthetic) Morphing airfoils (Credits: Risoe DTU)

Alleviate temporal and spatial bandwidth issues Complexity/availability/maintenance All sensor-enabled control solutions:

7 Active Load Mitigation: Limits and Issues Pitch control: Limited temporal bandwidth (max pitch rate 7-9 deg/sec) Limited spatial bandwidth (pitching the whole blade is ineffective for spatially small wind fluctuations) Distributed control: Alleviate temporal and spatial bandwidth issues Complexity/availability/maintenance All sensor-enabled control solutions: Complexity/availability/maintenance Off-shore: need to prove reliability, availability, low maintenance in harsh hostile environments

8 Presentation Outline

advantages: no actuators, no moving parts, no sensors (if you do not have them, you cannot break them!

9 Passive Load Mitigation Passive control: loaded structure deforms so as to reduce load Two main solutions: - Bend-twist coupling (BTC): exploit anisotropy of composite materials - Swept (scimitar) blades Angle fibers in skin and/or spar caps Potential advantages: no actuators, no moving parts, no sensors (if you do not have them, you cannot break them!) Other passive control technologies (not discussed here): - Tuned masses (e.g. on off-shore wind turbines to damp nacelle-tower motions) - Passive flaps/tabs -

10 Objectives Present study: Design BTC blades (all satisfying identical design requirements: max tip deflection, flap freq., stress/strain, fatigue, buckling) Consider trade-offs (load reduction/weight increase/complexity) Identify optimal BTC blade configuration Integrate passive BTC and active IPC Exploit synergies between passive and active load control Baseline uncoupled blade: 45m Class IIIA 2MW HAWT

, stress/strain, fatigue, buckling) Consider trade-offs (load reduction/weight increase/complexity)

11 Presentation Outline

tapering, Cost: Blade weight (or cost model if available) Structural parameters: thickness of

12 Optimization-Based Multi-Level Blade Design Cost: AEP Aerodynamic parameters: chord, twist, airfoils Cost: AEP/weigh (or cost model if available) Macro parameters: rotor radius, max chord, tapering, Cost: Blade weight (or cost model if available) Structural parameters: thickness of shell and spar caps, width and location of shear webs Controls: model-based (selfadjusting to changing design)

Fine level: 3D FEM Coarse level: 2D FEM section & beam models Definition of sectional design parameters - ANBA 2D FEM sectional analysis - Computation of 6x6 stiffness matrices - Definition of

13 Fine level: 3D FEM Coarse level: 2D FEM section & beam models Definition of sectional design parameters - ANBA 2D FEM sectional analysis - Computation of 6x6 stiffness matrices - Definition of geometrically exact beam model - Span-wise interpolation When SQP converged SQP optimizer min cost s.t. constraints Constraints: - Maximum tip deflection - 2D FEM ANBA analysis of maximum stresses/strains - 2D FEM ANBA fatigue analysis - Compute cost (mass) - Definition of complete HAWT Cp-Lambda multibody model - DLCs simulation - Campbell diagram DLC post-processing: load envelope, DELs, Markov, max tip deflection Automatic 3D CAD model generation by lofting of sectional geometry Automatic 3D FEM meshing (shells and/or solid elements) Update of blade mass (cost) Analyses: - Max tip deflection - Max stress/strain - Fatigue - Buckling Verification of design constraints Constraint/model update heuristic (to repair constraint violations)

The Importance of Multi-Level Blade Design Thickness Normalized stress Trailing edge strip thickness Fatigue damage index Stress/strain/fatigue: - Fatigue constraint not satisfied at first iteration

14 The Importance of Multi-Level Blade Design Thickness Normalized stress Trailing edge strip thickness Fatigue damage index Stress/strain/fatigue: - Fatigue constraint not satisfied at first iteration on 3D FEM model - Modify constraint based on 3D FEM analysis - Converged at 2 nd iteration ITERATION 1 ITERATION 0 Peak stress on initial model ITERATION 1 ITERATION 0 Buckling: - Buckling constraint not satisfied at first iteration - Update skin core thickness - Update trailing edge reinforcement strip - Converged at 2 nd iteration Fatigue damage constraint satisfied Increased skin core thickness ITERATION 1 ITERATION 0 Increased trailing edge strip ITERATION 1 ITERATION 0

model ITERATION 1 ITERATION 0 Buckling: - Buckling constraint not satisfied at first iteration - Update skin core thickness - Update trailing edge reinforcement strip -

aluminum alloy for visual inspection of blade

15 2MW 45m Wind Turbine Blade Currently undergoing certification at TÜV SÜD CNC machined model of aluminum alloy for visual inspection of blade shape Design developed in partnership with Gurit (UK)

16 Presentation Outline

17 Fully Coupled Blades 1. Identify optimal section-wise fiber rotation Consider 6 candidate configurations Skin angle Spar-cap angle BTC coupling parameter: α = K BT K B K T

18 Fully Coupled Blades: Effects on Weight Spar-caps: steep increase Skin: milder increase Spar-cap/skin synergy Stiffness driven design (flap freq. and max tip deflection constraints): Need to restore stiffness by increasing spar/skin thickness

19 Fully Coupled Blades: Load Reduction Spar-cap/skin synergy: good load reduction with small mass increase

20 Fully Coupled Blades: Mechanism of Load Reduction

21 Fully Coupled Blades: Effects on Duty Cycle Less pitching from active control because blade passively selfunloads Much reduced life-time ADC

22 Partially Coupled Blades 2. Identify optimal span-wise fiber rotation: 5 candidate configurations Reduce fatigue in max chord region Avoid thickness increase to satisfy stiffness-driven constraints

23 Partially Coupled Blades: Effects on Mass Fully coupled blade Too little coupling

24 Partially Coupled Blades: Effects on Loads F30: load reduction close to fully coupled case

25 Partially Coupled Blades: Effects on Duty Cycle Best compromise: similar load and ADC reduction as fully coupled blade, decreased mass

26 Presentation Outline

27 Integrated Passive and Active Load Alleviation Individual blade pitch controller (Bossaniy 2003): Coleman transform blade root loads PID control for transformed d-q loads Back-Coleman-transform to get pitch inputs Baseline controller: MIMO LQR

28 Integrated Passive and Active Load Alleviation Two IPC gain settings: 1. Mild: some load reduction, limited ADC increase 2. Aggressive: more load reduction, more ADC increase Five blade/controller combinations: BTC: best coupled blade + collective LQR IPC1: uncoupled blade + mild IPC BTC+IPC1: best coupled blade + mild IPC IPC2: uncoupled blade + aggressive IPC BTC+IPC2: best coupled blade + aggressive IPC

29 Integrated Passive/Active Control: Effects on Loads Synergistic effects of combined passive and active control

30 Integrated Passive/Active Control: Effects on Duty Cycle Not significant: ADC very small here Same ADC as baseline (but great load reduction!)

31 Conclusions Optimization-based blade design tools: enable automated design of blades and satisfaction of all desired design requirements BTC passive load control: - Skin fiber rotation helps limiting spar-cap fiber angle - Partial span-wise coupling limits fatigue and stiffness effects Reduction for all quality metrics: loads, ADC, weight Combined BTC/IPC passive/active control: - Synergistic effects on load reduction - BTC helps limiting ADC increase due to IPC (e.g., could have same ADC as baseline blade with collective pitch control) Outlook: Manufacturing implications of BTC and partially coupled blades Passive distributed control and integration with blade design and active IPC control

32 Outlook: Testing with (WT) 2, the Wind Turbine in the Wind Tunnel Aeroelastically-scaled wind tunnel model of the Vestas V90 wind turbine with individual blade pitch and torque control Applications: Testing of advanced control laws and supporting technologies Testing of extreme operating conditions Tuning of mathematical models Aeroelasticity and system identification of wind turbines Multiple wind turbine interactions Off-shore wind turbines (moving platform actuated by hydro-structural model) 13.8x3.8m, 14m/s, civil section: Turbulence < 2% With turbulence generators = 25% 13m turntable Aeroelastically scaled blades (70g, 1m) 4x3.8m, 55m/s, aeronautical section: Turbulence <0.1% Open-closed test section Civil-Aeronautical Wind Tunnel of the Politecnico di Milano Pitch actuator: Zero backlash gearhead Built-in encoder Main shaft with torque meter Pitch actuator electronics Rotor sensor electronics Slip ring Torque actuator: Planetary gearhead Torque and speed control Conical spiral gears

33 Outlook: Testing with (WT) 2, the Wind Turbine in the Wind Tunnel Turbulence (boundary layer) generators Good aerodynamic performance even at low Reynolds Blockage correction verified by RANS CFD

Load-reducing controllers Floating platform effects on stability 6 DOF moving platform Platform motion WT response Proof of

34 Outlook: Off-Shore Aero-Elastic Model Goal: aeroelastically-scaled wind tunnel model of off-shore wind turbine with individual blade pitch and torque control Applications: Testing of control laws Damping enhancement controllers Load-reducing controllers Floating platform effects on stability 6 DOF moving platform Platform motion WT response Proof of concept, 2 DOF hydraulic actuation (prescribed) Real-time PC running mathematical model of wet part of offshore machine (hydro-elastic model)

Acknowledgements Thanks to the POLI-Wind team! Special thanks to M. Bassetti, P. Bettini, M. Biava, F. Campagnolo, S. Calovi, S. Cacciola, F. Cadei, G. Campanardi, M. Capponi, A. Croce, G. Galetto, L.

35 Acknowledgements Thanks to the POLI-Wind team! Special thanks to M. Bassetti, P. Bettini, M. Biava, F. Campagnolo, S. Calovi, S. Cacciola, F. Cadei, G. Campanardi, M. Capponi, A. Croce, G. Galetto, L. Maffenini, P. Marrone, M. Mauri, S. Rota, G. Sala, A. Zasso of the Politecnico di Milano Funding provided by Vestas Wind Systems A/S, Clipper Windpower, Alstom Wind, Italian Ministry of Education, University and Research

tecnico lano POLI di MI tecnico Short Course on Wind Energy - Multidisciplinary Design of Wind Turbine Blades -

tecnico lano POLI di MI tecnico Short Course on Wind Energy - Multidisciplinary Design of Wind Turbine Blades - di MI tecnico POLI tecnico lano Short Course on Wind Energy - Multidisciplinary Design of Wind Turbine Blades - Carlo L. Bottasso Politecnico di Milano, Italy November 2011 Presentation Outline Holistic