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༌12षզIEA Wind‫׿ؔװ‬՛էߦே఑‫ףؠ؂ם‬՛৿৘ӡIEAӢ֮ӣᆭ፛ਙട੤፛،‫(ؕ؞ץآ‬ጸൺӱIEA Wind TCP)‫ב‬೗௹֖֥փ‫׊‬ӧTask44ӱ‫׾بיכ‬؊‫ז‬՛֭ؕጺ‫׋‬඘ฑ୞
䕔Operating Agents
Paul Fleming (NREL), Jan-Willem van Wingerden (TU Delft)
䕔Web Page
https://iea-wind.org/task44/
օ֠֟֞֊֭‫׉‬ၾဂ ୽ਈࣤዿ፛ॺଞ੄സӹ௺ภईႷጺ໲‫ףؠ؂ם‬՛ଞ੄‫״بװ‬՛ӹዼඑᆭ፛‫ףؠ؂ם‬՛ஸဗጦዿᇀኾӹੱ ೨଍ኃ ӱ ዼඑᆭ፛ଞ੄ੱࡱ‫״بװ‬՛ӹؓ‫צ׮׶ؠ‬՛‫ؠ‬ዼඑᆭੳଞ੄ᆪ኷ӹᆪ኷ྙ

2IEA Wind TCP Task44
What is the wind turbine wake phenomenon ?
WakeStructure:SideViewડೱ་ॺࣤዿ፛ॺଞ੄സࣽဗถພᆭၒ֪֥వࢬ
2020Ⴉ11ଈ12႘

3IEA Wind TCP Task44

A turbine, standing in the wake of another turbine, will experience a higher • Photograph taken by
structural loading due to a higher turbulence intensity, and will not extract theChristian Steinus in 2008,
same amount of energy as a turbine standing in the free wake.
photo taken on thespecial atmosphericconditions.
• And what we nicely cansee in this photograph isthat there is interaction
between turbines, sowake interaction.

Wind farm control is an active andgrowing field of research in which thecontrol actions of individual turbines in awind power plant are coordinated tominimise wake interactions.

‫׾بטכ‬؊‫ז‬՛ؕฑ୞֮Ւᆭ፛ᄇဃസၾ଼֭y֭ᆭೆ֭ฑ୞၌ఢ‫ྔב‬ธ֖֥Ւᆭೆ‫֭פיםכ‬ະୗఢዿ‫ב‬௻൚଺֪ጌև‫׊‬Ւঊᄇ֦ถྙ֖֥փ‫׊‬ଞ੄ᇀኾ֦ց‫׊‬Փ

IEA Wind TCP Task44
 ே఑‫ףؠ؂ם‬՛৿৘ӡIEAӢ֮ӣᆭ፛ਙട੤፛،‫ؕ؞ץآ‬ӡጸൺӱIEA Wind TCPӢ‫ב‬೗௹֖֥փ‫׊‬ӧ IEA Windேၾ࡙ࡿवӡ࿙ዃ৿৘ӱNEDOӢ֊‫֭׈‬෮๬Քලਢ‫ב‬૪֥ӣIEA Wind֭ே఑੢၍ଞ੄ঊ၌
ӡTask 44Ӣ֪ౄब֖֥փ‫׊‬ӡ2021Ⴉ12ଈ15႘⿤Ӣӧ

Task 44 (Wind Farm Flow Control) ֭‫ר׮‬՛،• ᆭ፛ᄇဃസ֭ฑ୞ᇀኾ֪։֐‫׊‬ே఑੢၍ଞ੄• ᆭ፛ᄇဃസ֭ฑ୞‫֧ؕׯ؟שؠח‬ตႷ୶එַ֭஭ଝ֋൳࿾
(ଞ੄֭ኬ࿨)9 ᆭ፛ᄇဃፘ֭ໃౌ֪‫׊ׇ‬ဃ፛‫׃ؕ׻׮׬‬ဃ፛౦඘֪։֐‫׊‬ᆭ፛ᄇဃ֭अཏ֭௻་ँ
9 ᆭೆ֭‫פילכ‬झഓ֭࿃ଲ֪‫׊ׇ‬Ւᆭ፛ᄇဃ‫֭׽׮ר‬࿃ଲँ
9 ᆭ፛ᄇဃസ֭ฑ୞‫ؓ׶ب؎֭ؠ׼ؗ‬՛‫׽׮؎֧פ‬،‫्֭׮ט׻פ؞‬ᄇՒ؎‫׽׮‬،‫׮ט׻פ؞‬Ք‫׼‬՛‫״‬୪হ້֭ු

4

5IEA Wind TCP Task44
႘቎֭໲ฑ೗֩ౄइ৿৘• ડೱ་ॺ/ၾဂ ୽ਈ (༉ᅳೄ)ዼඑᆭ፛ଞ੄֪৘֘‫׸׽॒ॆ׊‬،་ॺӹଞ੄സ֋ౄब• ‫ؘ׭‬؆‫ب‬Ք‫ؚ؀؟‬՛‫ח‬؋‫ؠ‬Ք‫׭׿ם‬՛, 4኎• ဪಷ‫ףؠ؂ם‬՛‫ׯؕ׻׮׬‬, 3኎• ႘ጵ່๫, 4኎• ဪੜ‫׮ס‬, 2኎ౄइேᇚேӣ‫׵ب؞ן‬ӣࢷேӣ؊‫׾ب؞بט‬ӣ ‫׾ب؞ؠיח‬ӣ‫ؓب׼‬՛‫פ‬ӣ‫بי؏׮‬ӣ ‫׹י׾‬ӣ ‫לכؠ؃‬՛ӣ႘቎

6Task44 ‫ؤ‬՛‫פ‬؆‫צ׸‬՛‫׭‬ӡWPӢଞ੄ถऐ֭೭ആ
ਙടՔ‫्֭ؕׯ؟שؠח‬ᄇ‫֪بיؖ‬WP2ַౄइ UncertaintyQuantification/୙☜ᐇᛶ䛾ᐃ㔞໬໢֭،‫֧֭׽פל׭آ‬ᎌૢ

7

WP2 ঊ၌ඡੳReviewandbestpracticesforwindfarmcontrolfieldvalidation

Paul Fleming, Task 44 Operating Agent(paul.fleming@nrel.gov)

8

9

䐟
䐠IEA Wind Task 44 Wiki

https://ieawindtask44.tudelft.nl/
䐡LinkedIn

https://www.youtube.com/@ieawindtask44talks21

https://www.linkedin.com/company/ieaͲwindͲtaskͲ44ͲwindͲfarmͲflowͲcontrol/1011ᆭೆ‫פיםכ‬ଞ੄वӱ 2021.4⿤2023.3
IEA Wind TCP Task44 ֧֭ᎌૢ‫׾بטכ‬؊‫ז‬՛֭ؕጺ‫׋‬඘ฑ୞ଞ੄वӱ 2023.4⿤2025.3
ঊ၌ၾዴ(2֊ଈ֪1ဗ‫्֪֨׀ב‬௹) : Àॆ॒֭၌୶ྔ௮

Àேၾ֭ගሇ੢ዛ / ගሇᄇම12ॆ॒֭௻ශ၌୶
Wednesday July 7th 2021
Wind farms that can reduce the effect of wakes

࿭Task44䛾Operating Agent• The main objective is to get more energy
out of the wind farm, but also taking into
account that we don't want to increase
the loading too much on the individualturbines.
• We typically build a first principles model.
But the first principle model doesn't
capture all the dynamics of a real windfarm or a real wind turbine.

䠄Lab HP䠅https://www.janwillemvanwingerden.nl/

Quote source: TWIND Summer School - Session_Morning_07_07
More info: https://twindproject.eu/• So what we believe is that we always
h a v e t o a u g m e n t our m o d e l w i t hmeasurements. And again, we use here
the data driven control approach.

TWIND aims to create a network of excellence that will dynamize a pool of specialized research professionals and trainers in the domain of offshore wind energy.

13

Reconstruction windfield• In reality, we don't have access to the flow information.
So the only thing that we have is measurements. So weonly have measurements on a turbine level.

Predictionwindfield• So what we have to do if we want to do wind farm control
and maybe this repositioning (floating). And we have toreconstruct the wind field.

RealͲtimeoptimization• Then if we have the wind field we have to predict what's
going on.
• And then we have to optimize. And then we can do arepositioning (floating). But we can also use alreadyexisting control technology on a turbine to maximize theenergy capture and or to reduce the structural loading in
the wind.

14
Static wake control concepts rely on affecting the wake throughrelatively slow changes of the control degrees of freedom of theturbine in a way that affect the time-averaged properties of the wake.

QuasiͲsteadyflowcontrol
AxialͲinductionͲbasedcontrol•

Induction control (ා଑਑౪) is basically derating (଀෰଩੖).

•If we derate the first turbine, then it will let energy go. And thenwe hope that the energy that we let go will be picked up by theturbines in the wake.

•We hope that the sum of the energies will be bigger than thegreedy approach.

•Now recent results basically show that actual induction control
doesn't work. So the energy that we let go is not picked up by all
the other turbines.

•High-fidelity wind tunnel and field tests have shown that thebenefits of induction control are very limited.

15
Static wake control concepts rely on affecting the wake throughrelatively slow changes of the control degrees of freedom of theturbine in a way that affect the time-averaged properties of the wake.
•On the other side, there is another existing control technology
which is called wake steering.

•We misalign the first turbine with the main wind direction.

•If we misalign the turbine, we can redirect the wake. So what wetry to do is that the wake doesn't hit the turbines downstream.

•This technology seems to have a lot of potential in wind tunnel
experiments, high fidelity simulations, and field experiments.

QuasiͲsteadyflowcontrol
Wakesteeringusingyawoffsets

16
QuasiͲsteadyflowcontrolWakesteeringusingyawoffsets•

At the left, we see the baseline. And atthe right, we see the optimized setting.

•The optimized setting meaning that we
had a first principles model, in this case
the FLORIS.

•Then we optimized the optimal YAWsettings. And what we nicely see is that
we can do wake steering.

•Now in the graph, we can also see thepotential and the amount of energythat we can now extract out of a windfarm (Of course, this is a high fidelity
simulation).

•The wind conditions are more or lesssteady. It's the same inflow direction
and also the turbulence intensity ismore or less the same.

•So we believe that also if we reallywant to apply wake steering in a wind
farm, we also have to make it morerobust with respect to time varying
conditions.

OpenͲloopwindfarmcontrolscheme

ClosedͲloopwindfarmcontrolscheme

17Keywords:datadrivenapproach,
MPC(䝰䝕䝹ண ไᚚ)principle⿵㊊ࢨྫͫ͜Ν͢ͱϓΡʖχώρέΝखΔ͵͏੏‫ޜ‬ྭ εʖίϱη੏‫ޜ‬ʁ͍Δ͖ͣΌఈΌΔΗͪॳ঄Ͷॊͮͱ੏‫ޜ‬͹֦ஊ֌Ν஠࣏਒Όͱ͏͚੏‫ޜ‬ʱϕϧϱφ͹ड़ྙ஍ ଎ఈ஍ Νड़ྙ͢ɼͨΗΝϓΡʖχώρέͦ͠ɼ೘ྙ஍ͳड़ྙ஍Ν৙Ͷർֳ͢ͱྈंΝҲ஗ͦ͠ΖΓ͑Ͷસର͹૤ࡠྖΝ௒੖ͤΖ੏‫ޜ‬WakeͲMixing

KWWSVZZZDOWH[FRUSFRMSFRQWUROV\VWHPZRFRQWUROV\VWHP

18• Here, we focus on the challenges related
to triggering and interacting dynamicallywith wakes and turbulence as a means tocontrol wind farm flow and to speed up
wake breakup or to increase mixing andentrainment into the wakes.
• Recently, some first studies suggest that
this effe ct may b e l everaged tosignificantly increase the energy
extraction of wind farms.

NormalͲoperationwake(Periodic)dynamicinductioncontrolHelixapproach

Illustration from an LES study (SOWFA), 10MW Wind Turbine
Darkblue shadingcorrespondstoanisosurfaceofthevelocity;
lightblue shadinginthehorizontalplanecorrespondstovelocitymagnitude.WakeͲMixing<<• However, then still you have to do all the safety checks and look at all the loads.
• So implementation wise, on paper it seems easy, but for industry it's quite a big step.

Wake-Mixingડೱ་ॺՔၾဂଞ੄ಬ֦֭೘ຎ)ORZइවົམลఢ֖Ւᆘ໲֭၌ዷӲ؉‫ࢥ׶׸‬၌Ӵ‫௺ב‬ଵ• Pitch motion amplitude: ±5 [deg]• Pitch motion period: 0.8 [s]20

The power output of downstream WT | Methods••࣒ ൌ ૙°, ൅૜૙° െ ૜૙°•࣒ ൌ ૙°The power generation performance of thePitch motion•Fixedwind turbine was evaluated by varying the21

blade rotation speed.

7RSYLHZ

Parameter

Value

Inflow wind speed [m/s]10

Rotor diameter [m]0.442

Tip speed ratio of the upstream wind turbine [-]3.6

Rotor speed of the upstream wind turbine [rpm]1556

Sampling frequency [Hz]1000

Sampling time [s]60Pitch motion amplitude [deg]5Pitch motion period [s]0.8

The power output of downstream wind turbine•

The power generation performance of the windturbine was evaluated by varying the bladerotation speed.

•Torque was measured and power output was

ࡼcomputed.
ൌ ࢀ࢘ ൈ ࣓ܲ ሾWሿ
‫ ؟‬Powerܶ‫ ݎ‬ሾNmሿ ‫ ؟‬Torque߱ ሾrad/sሿ ‫ ؟‬Angular velocity

4.6% increase

When yaw angle is 0°, the power
output of the downstream WT isincreased by pitch motion of theupstream WT.

22https://www.youtube.com/watch?v=DEERw41UU08&t=6s2022Ⴉ

Wind farm control has been a topicof research for more than twodecades. It has been identified as acore component of grand challenges
in wind energy science to support
accelerated wind energy
deployment and transition to aclean and sustainable energy system
for the 21st century.
䖃ᆭ፛‫ףؠ؂ם‬՛ऎॺ֪։֐‫׊‬ບ་֩ड༎֭䖃ཨ७ጅຍ֧֖֥ၟ࿊֔‫֥׋‬փ‫׊‬ӧ1Introduction
1.1Maincontrolobjectivesstudiedtodate
1.2Controlapproaches
1.3Outline
2Windfarmflowcontrolphysics:turbulence,wakesandtheatmosphericboundarylayer
2.1QuasiͲsteadyfloweffects
2.1.1AxialͲinductionͲbasedcontrol
2.1.2Wakesteeringusingyawoffsets
2.2Wakedynamicsandturbulence
2.2.1Wakedynamics
2.2.2BoundaryͲlayerturbulence
2.3Mesoscaleeffects,blockageandwindfarmwakes
3Controlalgorithms

䖃

3.1Currentpractice— OpenͲloopcontrol
3.2TheclosedͲloopparadigm
3.3Synergieswithartificialintelligenceandotherdigitalization
concepts
3.4Controllability,observability,andsensors

䖃4Validationandindustrialimplementation

䖃䖃

Overview of wind farm flow control concepts andimportant elements that play a role23ౄ ண ᇌ ଝ

In summary, the field of wind farm
flow control is an active area of
research and innovation, with many
interesting multidisciplinaryc h a l l e n g e s (ͤ) , a n d e x c i t i n g
prospects for the increase of thetotal value of wind energy forsociety.
࿭ॺ఑࿨֩ड༎

4.1ProofͲofͲconceptstudiesinhighͲfidelitysimulationtools
4.2Validationinwindtunnelexperiments
4.3Validationviafieldtests
4.4Industrialimplementation
5Integrateddesignandsystemsperspective
5.1Progressinwindfarmdesignoptimizationresearch
5.2WindfarmcontrolcoͲdesign(CCD)
5.2.1WindfarmCCDforAEPandLCoE objectives5.2.2WindfarmCCDforprofitabilityobjectives
5.2.3WindfarmCCDfornonͲeconomicobjectives
6Conclusions