1March 14, 2011

Control of Wind Turbines:A data-driven approach

dr.ir. Jan-Willem van Wingerden

March 14, 2011 2

Outline

• General introduction

• Data driven control cycle

• ‘Smart’ rotor

• Visit NREL

• Conclusions and outlook

March 14, 2011 3

Outline

• General introduction

• Data driven control cycle

• ‘Smart’ rotor

• Visit NREL

• Conclusions and outlook

March 14, 2011 4

General introduction: overview

Civil MechanicMaritime

ElectricalMath

IndustrialDesign

Manage-ment

AppliedScience

Architec-ture

Aerospace

60 fte, 35 PhD

Wind energy

Delft Center for Systems and Control

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General introduction: Ph.D. within DUWIND

experiments / analysis & design /

advanced NavierStokes

Bijl, van Bussel

van WingerdenvanKuik Bersee

Analysis, feasibility, lab tests / small scale field-test /

large scale field-test

Support structures, access systems, Floating offshore

(combined with wave energy?)

Willemse, vdTempel

grid connection & integration, market & institutions, ensemble with other sust.energy sources in DRI-TUD

Kling, Kunneke

Multidisciplinary design methods, component and system design, electric power conversion,

Zaaijer, vanTooren, Tomiyama,Polinder,

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Control: Long history in the windBongers (PhD)

Baars (PhD)Steinbuch (PhD)

Molenaar (PhD)

Bosgra (Prof.) Verhaegen (Prof.)

Wingerden (PhD => assistant prof.)

General introduction: DCSC

Gregor Baars

2008

2003

1989

1994

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General introduction: My Ph.D. students

•Ivo Houtzager (4th year Ph.D.): adaptive and learning control algorithms for wind turbines

•Gijs van der Veen (2nd year Ph.D.): Data driven control of wind turbines with ‘smart’ rotors

•Patricio Torres (1st year Ph.D): Wind farm control (distributed computation)

•Two new Ph.D. positions (1. combined structural and control optimization, 2. Fault tolerant wind farm control)

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Outline

• General introduction

• Data driven control cycle

• ‘Smart’ rotor

• Visit NREL

• Conclusions and outlook

March 14, 2011 9

Data-driven control: Conventional

Model for control based on first

principles

Controller design algorithm

Traditional design approach

"..direct validation of models describing wind energy conversion systems by a direct comparison with measured data is of very limited use. One of the few possible solutions to this problem is the application of system identification.“ (bongers, 1994)

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Model for control based data

bbbbb

Controller design algorithm

Data driven design approach

Data comes in

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Data driven approach: Sys. ID

• Obtain model from measurement data

• System operates in closed loop

• Multiple-Inputs and Outputs

• Required external perturbation signal

• Which model structures are relevant?

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Data driven approach: Model structure

• Nonlinear (pff, really hard)

• Linear Time Invariant (LTI) (using linearization)

• Linear Parameter Varying (LPV)

• Hammerstein Models

Gijs van der Veen

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Data driven approach: Model structure

Gijs van der Veen

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Outline

• General introduction

• Data driven control cycle

• ‘Smart’ rotor

• Visit NREL

• Conclusions and outlook

March 14, 2011 15

‘smart’ rotor

Wind energy:

• Young technology

• Rapid growth

• Too expensive

Current desire (again) increasing size:

• Offshore (cost foundation)

• Power (with the square)

Solution: New control concepts (and design methodologies)

Variable speed turbine

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‘smart’ rotor

Using integrated flaps

SMAPiezo

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WT experiments I:

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• Wind tunnel

• Blade

• Pitch system

• Trailing edge flap

• Sensors

• Real-time system

WT experiments I:

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First Principles vs

experimental modeling

We applied traditionalloop shaping

WT experiments I:

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• Feedforward control

• Feedback control

• Periodic disturbance

• Random disturbance (turbulence)

V= 30 m/s

α= 6 degrees

3P excitation

WT experiments I: Experimental results

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• Feedforward control

• Feedback control

• Periodic disturbance

• Random disturbance (turbulence)

V= 30 m/s

α= 6 degrees

Eigenfrequency

WT experiments I: Experimental results

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• Feedforward control

• Feedback control

• Periodic disturbance

• Random disturbance (turbulence)

V= 30 m/s

α= 6 degrees

Eigenfrequency flap excitation

WT experiments I: Experimental results

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• Feedforward control

• Feedback control

• Periodic disturbance

• Random disturbance (turbulence)

V= 30 m/s

α= 6 degreesOutput spectrum

Input spectrum

WT experiments I: Experimental results

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WT experiments II: Plans

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WT experiments II: Real

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Data-driven approach: Periodic disturb.

• Black without excitation• Gray with

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Data-driven approach: Periodic disturb.

• Black id with period• Gray id without

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Data-driven approach: Feed Forward

Parameterize input:

Lift the expressions over one period:

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WT experiments II: MIMO feedback

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WT experiments II: Time domain results

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WT experiments II:

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Outline

• General introduction• Data driven control cycle• ‘Smart’ rotor • Visit NREL

• Vibrations CART III• Drive train damper CART II

• Conclusions and outlook

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NREL: instability I

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NREL: instability II

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NREL: Uhm, where is it coming from

1. Unstable control law Not likely

2. Negatively/badly damped mode

Maybe

3. Aeroelastic instability Maybe

4. Stall operation Not likely

5. P loads on top of badly damped structural modes

Probably a part of the problem

6. The drive Maybe

7. Something else We hope not

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NREL: Linear model (FAST new)

1p

2p

3p

4p

5p

6p

7p

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NREL: Linear model (FAST old)

1p

2p

3p

4p

5p

6p

7p

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NREL: FFT vs rotor speed

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NREL: FFT vs rotor speed

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NREL: conclusion

Is it badly damped combined with an excitation (4P)?

Solution limit max RPM!!

Other arguments to do this =>

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NREL: 55 HZ (HSS Torque)

LSS RPM

Fre

quency

(Hz)

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NREL: 109 HZ IMU X

LSS RPM

Fre

quency

(Hz)

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Outline

• General introduction• Data driven control cycle• ‘Smart’ rotor • Visit NREL

• Vibrations CART III• Drive train damper CART II

• Conclusions and outlook

March 14, 2011 44

NREL 2: CART II

LQR two inputs (ss tower accel, HSS speed)

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NREL 2: Before/after drive train replacement

Can we really talk about damping??

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NREL 2: What is going on??

1. The LQR works perfect in simulation (even with a different stiffness)

2. Typically you see this response if there is a time delay

E-stops

(sample time 100Hz)5 samples (0.05 s) will make a difference

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NREL 2: Solution

Compensate for the delay (Pade)Generator speed Tower ss acceleration

Genera

tor

Toque (a

mpl.)

Genera

tor

Toque (p

hase

)

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NREL 2: Conclusions1. Simulations seem to support robustness issue

2. Waiting for wind

3. Robust control the way to go??

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Outline

• General introduction

• Data driven control cycle

• ‘Smart’ rotor

• Visit NREL

• Conclusions and outlook

March 14, 2011 50

Conclusions….

• data driven algorithms are widely applicable

• Hammerstein model structure the way to go??

• the ‘smart’ rotor has the potential to reduce costs

• Waterfall plots are a really strong tool

• Delays can destroy performance

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Outlook….

• ‘smart’ rotor: do new wind tunnel experiments

• sys. id. quantify uncertainties (for robust controller design)

• incorporate my research in education activities

• waiting for wind to validate our NREL work

• etc. etc..

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NREL 2: Other modifications1. LQR with frequency weights

2. Hinf control

3. Robust control