Current perspectives on wind turbine control - Bossanyi (2012).pdf

Workshop on Sustainable Control of Offshore Wind Turbines, University of Hull

Current perspectives on wind turbine controlErvin Bossanyi, 19th September 2012

Overview

• What is a wind turbine controller?• Power production control: objectives• The operating curve• Closed loop control and design methods• Examples• Sensors, actuators and reliability• Future perspective

What is a wind turbine controller?Sensors ActuatorsAlgorithms

Flexibleblades

Flexibleshafts

Pitchactuators

Flexibletower

Flexiblemountings

Power andspeedtransducers

Controlalgorithms

Sensors:•Power•Rotational speed•Loads•Accelerations•Wind speed•Yaw error

Actuators:•Pitch demands•Torque demand•Yaw demand•Brake on/off•Contactor on/off

Wind

Waves

What is a wind turbine controller?• Supervisory control

• Sequence control, stops/starts, etc.• Alarms, fault handling• Yaw control• External communications (operator interfaces, SCADA)

• Power production control• Main topic of talk• Adjusting generator torque, blade pitch• Overlaps with supervisory control: yaw / set-point adjustments / fault tolerance

• Safety system• NOT part of controller, but closely related• Takes over if the controller isn’t coping• “Dumb” failsafe hardware – trips & relays

Main turbine control types

Fixed speedstall regulated

Variable speedpitch regulated

Variable speedstall regulated

Variable slip

Fixed pitchActive pitch control:

• Full-span• Partial-span• Distributed control?

Fixed speedpitch regulated Passive generator

torque variation

Active control ofgenerator torque

This talk (and mostutility-scale turbines)

Nothing tocontrol

Overview


5

Power production controlController objectives• Maximise energy production

• Applies mainly below rated wind speed• Minimise (or manage) the loads

• Keep fatigue loads down• Avoid excessive actuator duty• Avoid trips and unnecessary shutdowns (especially using the safety system)• Avoid loading peaks where necessary• Deal with extreme load scenarios

These objectives conflict: need to understand the trade-offs, but• Sacrificing energy is very expensive … even 0.1% loss of annual production would

need very good justification!• “Optimising” the trade-off is not really possible – depends on detailed component

cost models, energy prices, site conditions, discount rates …

Overview


6

The operating curve

Constant power line

Steady state controller - Optimal Mode Gain

λω

λ GRTipSpeedU g==

33

353

22 GCRACU

P gpp

λωπρρ

==

233

5

2 gp

gd G

CRPQ ωλ

πρω

==

Optimum Cp below rated: quadratic torque-speed curve

U = Wind speedλ= Tip speed ratioωg = Generator speedR = Rotor radiusG = Gearbox ratioP = PowerCp = Power coefficientρ = Air densityA = AreaQd= Demanded gen. torque

Steady power curve

-5

0

5

10

15

20

25

0 5 10 15 20 25

Wind speed [m/s]

Electrical power [MW]

Pitch angle [deg]

Rotor speed [rpm]

Thrust force [10^5N]

The wind turbine power curve

0

1

2

3

4

5

6

0 10 20 30 40

Wind speed (m/s)

Pow

er (M

W)

Turbulent (Class 1C)Minimum RPMMaximum CpMaximum RPMAbove ratedStorm control

Improved peakCp tracking?

Speed exclusionzone?

Fine pitch schedule(Thrust clipping?)

Fine pitchschedule

}Yaw tracking?

Cyclic pitch?

Dynamic fine pitchTransient overpower?

}Cut-in/cut-out hysteresisSet-point reduction?

Set-point reduction?Asymmetrical rate limits?

Network constraints, e.g. power reserve margin?

Cut-in/cut-outhysteresis

Overview


12

Closed loop control – SISO or MIMO?

Generator speed Generator torqueSpeedregulation: PI+

Generator speed,SS acceleration

Generator torqueVibrationdamping

Generator speed Collective pitchSpeedregulation: PI+

Fore-aftacceleration

Collective pitchTower vibrationdamping

Wind vane Yaw rateYaw control

Coupling!

Transitions

MIMO?

Loads e.g. blade Individual pitchLoad reduction:PI+ (d-q,1P,2P)

Generator speed Generator torqueCP trackingTransitions

Measurement Actuation demand


Generator speed Generator torqueCP tracking

Control design methods• Classical: SISO (but can be extended to deal with couplings)

• Extended PI(D)• Other filters

• Model-based: naturally handles MIMO cases. Many flavours, e.g.• LQG• H∞

• DAC• MPC

• Other: Fuzzy logic, neural network• May be useful for complex systems with unknown dynamics

Classical control – examples• Drive train damping• Speed regulation – torque & pitch*• Tower damping*• IPC*• LiDAR

• Collective pitch• IPC• CP tracking• Yaw control

*Including field test results

Controller tuning

• Use a linearised model of the turbine• Understand wind turbine dynamics and possible resonances• Measures of performance: open and closed loop responses• Damping of resonances• Apply gain schedule for different operating points• Test using detailed simulation model• Field evaluation is important

Campbell diagram

Linear model measures of performance

• Stability margins:� how far are we from the point where the system becomes unstable?

• Step responses:� e.g. how pitch angle and tower motion respond to a step change in wind speed?

• Frequency responses:� how much of the wind variation is being controlled away?� how much the pitch responds at the blade passing frequency, or the drive train

frequency?� how much the tower will be excited by the wind?

Overview


20

Drive train damper

Drive train damper: Bode plots

Frequency (rad/sec)

Phas

e (d

eg)

Mag

nitu

de (d

B)

-40

-20

0

20

40

60

80From: Generator torque demand

To: G

earb

ox to

rque

UndampedDamped

10-2

10-1

100

101

102

-360

-270

-180

-90

0

To: G

earb

ox to

rque

Generator speed Generator torqueBandpass filter

Drive train dampingNo damping

Gea

rbox

torq

ue [k

Nm

]

E

lect

rical

pow

er [k

W]

Time [s]

0

100

200

300

400

500

600

700

0 5 10 15 20 25 30

With damping

Gea

rbox

torq

ue [k

Nm

]

E

lect

rical

pow

er [k

W]

Time [s]

0

100

200

300

400

500

600

700

0 5 10 15 20 25 30

Speed regulation



Below rated (pitch = fine pitch):

Above rated (torque = rated torque):

Both loops attempt to regulate to the same set-point, so they will interferewith each other.Can decouple the loops e.g. by manipulating set-points for each loop:

• Above rated: depress torque loop set-point to force torque demandto upper limit (rated torque)

• Below rated: increase pitch loop set-point to force pitch demand tolower limit (fine pitch)

Gain scheduling

d (Torque) / d (pitch angle)

[kN

m/ra

d]

Pitch angle [deg]

-1000

-2000

-3000

-4000

-5000

-6000

-7000

-8000

0

1000

-5 0 5 10 15 20 25 30


Speed regulation – bells & whistlesGain scheduling as aboveNotch filters to avoid responding to structural resonances and nP forcingLoop-shaping filters to achieve desired stability marginsLow-pass filters to reduce sensitivity to measurement noiseVariable position limits

• Vary torque upper limit to maintain constant power• Vary fine pitch for power optimisation or thrust clipping• Dynamic fine pitch to prevent rapid thrust changes and reduce tower vibration• De-rating in high winds• Care with integrator desaturation – actually very straightforward

Variable or asymmetrical rate limits• E.g. for dynamic de-rating in high turbulence

Non-linear bolt-on terms• Additional pitch action triggered by large speed excursions or accelerations

Speed regulation

0100200300

400500600

0 100 200 300 400 500 600

Time [s]

468

10121416182022

0 100 200 300 400 500 600

Time [s]

283032343638404244

0 100 200 300 400 500 600

Time [s]

-202468

10121416

0 100 200 300 400 500 600

Wind speed, m/s

Rotor speed, rpm

Power, kW

Pitch, deg

NREL CART2, 4th February 2010

Speed regulation by torque – lowSpeed regulation by torque - highSpeed regulation by pitchOtherwise: variable speed operation(CPmax tracking)

Tower damping

“MISO” : Interacts with speed regulation• Only strongly at the tower frequency• Iterative design of the coupled SISO

loops

Fore-aftacceleration

Collective pitchTower vibrationdamping

FFKxxDxM δ+=++ &&&

x/FD

xDFF

p

p

&

&

β∂∂

−=δβ

−=δββ∂∂=δ

02050340 OFF12.42m/s 21.55%TI

02020007 ON12.09m/s 20.70%TI

Frequency [Hz]

9.0e+11

1.0e+08

1.0e+09

1.0e+10

1.0e+11

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Tower base bending moment spectrameasured on NREL CART2:

25

Individual pitch control

Loads e.g. blade Individual pitchLoad reduction:PI+ (d-q,1P,2P)

( )( )

( )( )

( )( )

π+π+

π+π+

=

3

2

1

q

d

LLL

3/4φsin3/4φcos

3/2φsin3/2φcos

φsinφcos

32

LL

Park’s transformation (3-phase electrical) = Coleman transformation (helicopters)

( )( )( )

( )( )( )

θθ

π+ϕπ+ϕ

ϕ

π+ϕπ+ϕϕ

=

θθθ

q

d

3

2

1

3/4sin3/2sin

sin

3/4cos3/2cos

cos

[ ]

=

θθ

q

d

q

d

LL

C

Reverse transformation

Controller (in non-rotating frame)

[C] could be diagonal with C11 = C22 = PI controller (+ notch filters etc.)

Individual pitch control• Straightforward generalisation to any number of blades (including 2)• Works in non-rotating frame where wind gradients are slowly-varying

• Simple and robust control loop design• Compensates for mean linear horizontal and vertical wind gradients across the rotor:

• Removes 1P peak in (rotating) blade out of plane loads• Removes 1P peak in (rotating) shaft bending loads• Removes 0P (mean) tilt and yaw moments (non-rotating)

Blade 1 Blade 2 Blade 3 Collectivepitch controller

Pitc

h an

gle

[deg

]

Time [s]

-2-4-6-8

02468

1012

180 190 200 210 220 230 240

• Removes 2P tilt and yaw moments (non-rotating) on 2-bladed turbines

• Increased pitch actuator duty & pitchbearing travel

• Not detrimental to power production

Individual pitch control – higher harmonics

• Removes 2P peak in (rotating) blade out of plane loads• Removes 2P peak in (rotating) shaft bending loads• Removes 3P tilt and yaw moments (non-rotating)

Load Pitch

Rotationaltransformation

(2P)


(2P)

D-axis control

Q-axis control

measurements(3 blades)

demands(3 blades)


(1P)


(1P)

D-axis control

Q-axis control

Rotational transformations easily generalised to multiples of rotor azimuthE.g. 2P-IPC: transformations using double the angles

Individual pitch control example1P & 2P IPC measured on NREL CART3:

OFF 'cart3 2011 05-1002-42-36'

ON 'cart3 2011 05-1002-57-36'

Blad

e ro

ot M

ysp

ectru

m [(

Nm

)²s]

Frequency [Hz]

1.0e+07

1.0e+10

1.0e+08

1.0e+09

0.0 0.5 1.0 1.5 2.0 2.5 3.0

OFF 'cart3 2011 05-1002-42-36'

ON 'cart3 2011 05-1002-57-36'

Shaf

t My

spec

trum

[(Nm

)²s]

Frequency [Hz]

1.0e+07

7.0e+09

1.0e+08

1.0e+09

0.0 0.5 1.0 1.5 2.0 2.5 3.0

OFF 'cart3 2011 05-1002-42-36'

ON 'cart3 2011 05-1002-57-36'

Hub

yaw

Mz

spec

trum

[(Nm

)²s]

Frequency [Hz]

1.0e+07

5.0e+09

1.0e+08

1.0e+09

0.0 0.5 1.0 1.5 2.0 2.5 3.0

OFF 'cart3 2011 05-1002-42-36'

ON 'cart3 2011 05-1002-57-36'

Pitc

h ra

te s

pect

rum

[rad²

s]

Frequency [Hz]

0.1

40000

1

10

100

1000

10000

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Blade root out of plane moment Shaft bending moment

Yaw moment at hub Pitch rate

Individual pitch control example

Hub My, SN4

0

100

200

300

400

500

600

700

800

900

1000

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Mean wind speed [m/s]

kNm

OFFON

Hub fixed Mz, SN4

0

100

200

300

400

500

600

700

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Mean wind speed [m/s]

kNm

OFFON

1P & 2P IPC measured on NREL CART3:Significant reduction in “damage equivalent” fatigue loads

With attention to detail, extreme loads can be unaffected (need to consider shut-downwith blades at different angles)

LiDAR-assisted controlLaser-Doppler anemometer: Laser beam projected forward from turbine providesadvance information about the approaching wind field

050

100150

-100-50

050

100-80

-60

-40

-20

0

20

40

60

80

050

100150

-100-50

050

100-80

-60

-40

-20

0

20

40

60

80

Many configurations• Scanning or multiple

fixed beams• Single or multiple

distances• Can also be blade-

mounted

33

LiDAR-assisted control• Improved energy capture due to better yaw tracking?

• Probably not much – but very useful for wind vane calibration!• Yaw control has to be slow (yaw motor duty, gyroscopic loads, etc.)• Pay attention to convention yaw tracking strategies first

• Improved energy capture due to better Cp tracking?• Tiny improvement, outweighed by large power & torque variations

• Reduced extreme loads due to anticipation of extreme gusts?• Promising but difficult to assess

• Reduced fatigue loads due to anticipation of approaching wind field?• Improved collective pitch control yields easy benefits• More marginal for individual pitch control

Reduced loads implies a potential for re-optimisation of turbine design→ Improved cost-effectiveness for future designs

Improved yaw tracking with LiDAR?

• Probably not much – but could be a very useful commissioning tool for wind vanecalibration!

• Yaw control has to be slow (yaw motor duty, gyroscopic loads, etc.)• Pay more attention to convention yaw tracking strategies first

15s

30s45s

0

2

4

6

8

10

12

14

0 0.02 0.04 0.06 0.08 0.1

Mean absolute yaw rate [deg/s]

RM

S y

aw m

isal

ignm

ent [

deg] No Lidar

Lidar

Mixed

10-minute simulation(but really dependson low-frequencyvariations which aresite-dependent)

Improved yaw tracking – how much potential is there?

A well-designed conventional yaw strategy may only lose 0.5% - 1%energy compared to “perfect” yaw control

Comparison of different yawing strategies

16º, 300s

16º, 150s

8º, 300s

Ideal Yaw

0

0.5

1

1.5

2

2.5

3

98.5% 99.0% 99.5% 100.0%

Annual energy production (%)

Mea

n tim

e be

twee

n ya

w

man

oeuv

res

(hou

rs)

(Simulation based on 6 years 10-minute average data with turbulence superimposed)

Improved CP-tracking with LiDAR?☺ Rotor speed tracks wind speed better� Needs huge power/torque swings to accelerate/decelerate rotor� Tiny fraction of % increase in power production – not worth it!

RPM(No Lidar) RPM(Lidar) Rotor average windspeed, m/s

m/s

or

R

PM

Time [s]

5

6

7

8

9

10

11

12

13

0 100 200 300 400 500 600

No Lidar Lidar

Elec

trica

l pow

er [

MW

]

Time [s]

0

1

2

3

4

5

6

0 100 200 300 400 500 600

Improved collective pitch control with LiDAR?

• Immediate improvement in speed regulation• Prefer to take the benefit by reducing control gains

→ Calmer pitch action→ Lower loads (especially tower bending moments)

Base PI Base PI + Lidar Reopt + Lidar Reopt, no Lidar

Rot

or s

peed

[rp

m]

Time [s]

10.0

10.5

11.0

11.5

12.0

12.5

13.0

13.5

0 100 200 300 400 500 600

Improved collective pitch control with LiDAR?

Blade root load reduction

00.5

11.5

22.5

33.5

44.5

Mx My Mz Fx Fy Fz

%re

duct

ion

SN 4 (Steel)SN 10 (GRP)

Shaft load reduction (SN 4)

0

2

4

6

8

10

12

14

Mx My Mz Fx Fy Fz

%re

duct

ion

Yaw bearing load reduction (SN 4)

-2

0

2

4

6

8

10

12

14

Mx My Mz Fx Fy Fz

%re

duct

ion

Tower base load reduction (SN 4)

-2

0

2

4

6

8

10

12

14

Mx My Mz Fx Fy Fz

%re

duct

ion

Even very simple methods achieve significant reduction in lifetime fatigue loads

Improved collective pitch control with LiDAR?Extreme load reduction is much harder to assess:• Extreme gusts not realistic – and how do they convect

and evolve?• LiDAR must be working at moment of extreme load

• Affected by meteorological conditions? (Fog, precipitation,lack of aerosols)

• Extreme gusts may not be design drivers• Now more emphasis on extreme turbulence:

0.001

0.01

0.1

15000 7000 9000 11000 13000 15000 17000

Tower base My (kNm)

Prob

abili

ty o

f exc

eeda

nce

Base case

LIDAR (typical range)0.001

0.01

0.1

135000 55000 75000 95000 115000

Tower base My (kNm)

Prob

abili

ty o

f exc

eeda

nce

Base case

LIDAR (typical range)

Improved IPC with LiDAR?

0 20 40 60 80 100 120 140

Blade root My moment (steel)

Blade root My moment (GRP)

Shaft My moment (steel)

Shaft Mz moment (steel)

Tower top nod moment (steel)

Tower top yaw moment (steel)

Increase in pitch travel

Decrease in loads or increase in pitch travel (%)

Conventional IPCLIDAR IPCBoth together

• LiDAR estimates the vertical & horizontal shear• Very simple strategy → some reduction of asymmetrical loads (without

needing load sensors)• Not as effective than using load sensors, but more sophisticated strategies

would be possible.

Model-based control• Combinations of observers/estimators (for system states and/or disturbances) with

‘optimal’ control action (i.e. to minimise some cost function)• Model of plant and/or disturbance dynamics gives a forward prediction of measured

signals, Starting from the current estimated state and the control actions justimplemented

• When those measurements become available, the prediction errors are used tocorrect the latest estimate of the state

• Cost function is a weighted sum of deviations of important variables from their idealvalues (expectations; maybe integrated over a finite future time horizon in the case ofMPC). Variables may include states, outputs, loads, control actions, etc., maybefrequency-weighted

• Control actions are calculated so that the cost function (J) is minimisedFind ui such that for all i0u/J i =∂∂

40

Model-based control: LQGKalman filter (state estimator)

Turbinedynamics

x(k -1)

u(k -1)

Correction

x'(k) x(k)

u(k)

y(k-1)

Optimalstate

feedbacky'(k-1)

Cost functionJ = xT.P.x + uT.Q.u

x’ = predicted statesx = state estimates

u = control signalsy’ = predictedmeasurements

y = measured signals

Kalman filter also includes a model of stochastic disturbances:• Sensor noise• Wind; e.g. integrated filtered white noise modulated by blade passing, etc.

LQG examples: 1P-IPC

[ ]

=

θθ

q

d

q

d

LL

C

• [C] is a 2-input, 2-output matrix• Still decoupled from collective

pitch• Cost function includes integrated

Ld & Lq as well as frequency-weighted d- & q-axis pitch rates

• Tricky to implement variable limitsand schedules (useful to preventunnecessary IPC in low winds,and to mitigate possible problemswith extreme loads)

Collective pitch

Differential using blade loads

Differential using shaft loads

Differential using yaw bearing loads

Differential, LQG

Blade root O/P

Shaft My

Yaw bearing My

Yaw bearing Mz

0

200

400

600

800

1000

1200

1400kNm

LQG: Torque & pitch controlSpeed regulation + tower damping using torque & pitchGenerator speed Generator torque

LQGFore-aftacceleration

Collective pitch

Cost function includes:• Collective pitch angle demand• Torque demand• Nacelle fore-aft displacement• Integrated generator speed• Frequency-weighted pitch rate

Practical complications:• Implementation of torque pitch and pitch

rate limits• Non-linearity: ‘fuzzy’ transitions between

controllers designed for different operatingpoints

• Always two controllers running in parallel• Interpolation using wind speed proxy (filtered

pitch angle or generator torque)

LQG: Torque & pitch control

SISO LQG

Nom

inal

pitc

han

gle

[deg

]

Time [s]

-2

0

2

4

6

8

10

0 100 200 300 400 500 600

SISO LQG

Gen

erat

orto

rque

[M

Nm

]

Time [s]

1.0

1.5

2.0

2.5

3.0

0 100 200 300 400 500 600

SISO LQG

Rot

or s

peed

[rpm

]

Time [s]

11.211.411.611.812.012.212.412.612.813.0

SISO LQG

Dis

plac

emen

tfo

re-a

ft [c

m]

Time [s]

15

20

25

30

35

40

0 100 200 300 400 500 600

Behaviour very similar to well-tuned SISO controllerSome reduction of tower loading (5% – 8% reduction above rated wind speed)

Model-based control

Difficult to ‘tweak’: adding filters, modetransitions, phasing features in and out,interaction with supervisory control, …Any change requires complete re-design.

Non-linearities can be troublesome:• LPV models / extended Kalman filters etc.• Piecewise linear with fuzzy transitions• Dealing with limits / constraints

Hard to design in practice! Cost functiondesign and optimisation is not so intuitive.Quadratic cost function not always “correct”:• Fatigue is non-linear• Speed only needs to keep below trip value

Numerically complex; difficult to implementCons

Handles MIMO very naturally• may become more important on large

flexible turbines with strong couplings• can make use of any available sensors

Intuitive cost functionMathematical rigour

Pros

Overview


45

Sensors, actuators and reliabilitySENSORS• Generator speed• Accelerations• Loads• Deflections?• Wind speed: anemometer (nacelle/hub) / along blade / Lidar• Yaw misalignment: wind vane (nacelle/hub) or LidarACTUATORS• Pitch actuators• Generator / power converter• Yaw motorsFailures may result in power reduction, down-time, increased O&M cost …

Consequences of actuator failurePitch actuator failure:• Pitch feathering is vital for safety.• “Pitch runaway” often drives extreme loads – but is it realistic? Actual cause

of failure is unspecified - really requires a proper FMEA.Torque actuator failure:• Loss of load: no worse than grid dropout. Turbine shuts down using pitch.• Short circuit→ large transient gearbox loadingYaw actuator failure:• Turbine shuts down – not urgent, could even wait until yaw misalignment is

excessive.

Consequences of sensor failureSensor redundancy:Wind speed: often 2 anemometers (and wind vanes); but turbine rotor is a largeanemometer!Generator / rotor speed, accelerations: redundancy is not difficult to achieve.Sensors for load reduction (e.g. blade strains, LiDAR): not essential for continuedoperation, but may need to reduce power set-point so as not compromise fatigue lifeuntil repair can be effected.Important: assumes failure is detectable. An undetected failure could cause damagingcontrol action. Failure detection:• Sensor has its own ‘sensor healthy’ signal• Controller may contain special algorithms to detect specific failures or general

abnormal operation

Sensor failure example: IPC• Uses blade root load sensors• Even if sensor failure is not notified to controller, relatively simple algorithms

comparing signals from the three blades can rapidly identify the fault• Consequences of realistic sensor failures are not serious: IPC load reduction

becomes less effective, but loads unlikely to become worse than with collective pitchcontrol

• Turbine can continue to operate in collective pitch control mode. If repair is notimminent (e.g. failure on remote offshore turbine in winter) then switch to reducedpower set-point to prevent excessive fatigue load accumulation. Suitable settingsshould be pre-defined to minimise energy loss while remaining within design loadenvelope.

Sensor failure example: IPCController can detect a failure, e.g. Dr = maximum normalised absolute difference ofpeak-to-peak load between the three blades:

Sensor failure example: LiDAR• LiDAR health signal should flag equipment failure or signal degradation due to

environmental conditions.• Controller should maintain independent ‘sanity check’ on Lidar signal.• Operation can continue, switching to conventional controller designed without LiDAR

input. If repair is not imminent (e.g. failure on remote offshore turbine in winter) thenswitch to reduced power set-point to prevent excessive fatigue load accumulation.Suitable settings should be pre-defined to minimise energy loss while remainingwithin design load envelope.

Overview


50

What next?• LiDAR control is in its infancy – much further development is possible• With LiDAR, MPC comes into its own (optimise performance over a prediction

window; forward predictions informed not only by known plant dynamics but also windinput preview information from LiDAR)

• Distributed blade control: probably retaining full-span pitch control as at present (alsoimportant for safety) but supplement it with local control along blade

• Sensors? Strain measurements & accelerometers along blade, Pitot tubes, Lidar, pressuretaps in blade surface

• Actuators? Flaps, microtabs, deformable trailing edges, air-jets … driven by piezo-electrics, SMAs, fluid pressure, etc…

• Mustn’t sacrifice reliability & maintainability!• Sensor/actuator failure: switch to conventional control (reduced set-point)• Repairing sensors and actuators out along the blade is difficult.

What next?• Rotor condition monitoring

• Detection of imbalances, changing natural frequencies, etc• Get more use out of any extra sensors• State estimation (Kalman filter)

• Wind farm control• One power station, not a collection of autonomous turbines• Turbines interact through their wakes• Optimise energy capture & fatigue loading across wind farm• Respond to external demands from network

Thank you for your attention

[email protected]

Current perspectives on wind turbine control - Bossanyi (2012).pdf

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Transcript of Current perspectives on wind turbine control - Bossanyi (2012).pdf