2024年2月28日(水曜日) ／ 11:50 - 12:25

第12回「IEA Windセミナー」
Task44：ウインドファームの流れ場制御■Operating Agents
Paul Fleming (NREL), Jan-Willem van Wingerden (TU Delft)■Web Page
https://iea-wind.org/task44/
うちだたかのり内田 孝紀
応用力学研究所/再生可能流体エネルギー研究センター/
洋上風力エネルギー高度利用分野/教 授
兼務 ： 洋上風力研究教育センター/
マルチスケール洋上風況研究部門/部門長

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IEA Wind TCP Task44
What is the wind turbine wake phenomenon ?
Wake Structure : Side View

九州大学応用力学研究所
温度成層風洞にて撮影
2020年11月12日

3IEA Wind TCP Task44

A turbine, standing in the wake of another turbine, will experience a higher • Photograph taken bystructural 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, so
wake 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.

ウィンドファーム制御は、風力発電所内の個々の風車の制御動作を調整して、風車ウエイクの相互作用を最小限に抑える、活発で成長している研究分野である。

IEA Wind TCP Task44
 国際エネルギー機関（IEA）は，風力技術協力プログラム（略称：IEA Wind TCP）を主催している．
 IEA Wind国内委員会（締約機関：NEDO）からの推薦・審議を経て，IEA Windの国際共同研究活動（Task 44）に参画している（2021年12月15日～）．

Task 44 (Wind Farm Flow Control) のスコープ• 風力発電所の制御分野における国際共同研究• 風力発電所の制御アルゴリズムと性能向上への貢献が焦点(研究の目的) 風力発電量の増産による電力システムや電力市場における風力発電の価値の最大化
 風車のウェイク荷重の低減による、風力発電コストの低減化
 風力発電所の制御モデルのベンチマークとベストプラクティスの開発、ベストプラクティス・データ交換の促進

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IEA Wind TCP Task44
日本の体制
主な参加機関• 九州大学/内田 孝紀 (代表者)

洋上風力研究に関する海外トップ大学/研究所が参画• ジャパン・リニューアブル・エナジー, 4名• 東芝エネルギーシステムズ, 3名• 日立造船, 4名• 東京ガス, 2名

参加国
米国，オランダ，英国，フィンランド， アイルランド，デンマーク，スペイン， ドイツ， ノルウェー，日本

6Task44 ワークパッケージ（WP）

研究成果の収集
技術・アルゴリズムの開発
メインにWP2へ参加 Uncertainty Quantification / 不確実性の定量化

他のプロジェクトとの連携

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WP2 活動状況

Review and best practices for wind farm control field validation

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

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①
②IEA Wind Task 44 Wiki

https://ieawindtask44.tudelft.nl/③LinkedIn

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

https://www.linkedin.com/company/iea‐wind‐task‐44‐wind‐farm‐flow‐control/

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風車ウエイク研究会： 2021.4～2023.3
IEA Wind TCP Task44 との連携

ウィンドファームの流れ場制御研究会： 2023.4～2025.3
活動内容(2か月に1度をめどに開催) : ■海外の動向調査■国内の情報共有 / 情報発信

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海外の最新動向
Wednesday July 7th 2021
Wind farms that can reduce the effect of wakes※Task44の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.

（Lab HP）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.

13Reconstruction 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.

Real‐time 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.

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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.

Quasi‐steady flow control
Axial‐induction‐based control•Induction control (誘導制御) is basically derating (負荷低減).

•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.

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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.

Quasi‐steady flow control
Wake steering using yaw offsets

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Quasi‐steady 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.

Open‐loop wind farm control scheme

Closed‐loop wind farm control scheme

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Key words : data driven approach,
MPC (モデル予測制御) principle
補足

指令だけをしてフィードバックを取らない制御(例)シーケンス制御：あらかじめ定められた順序に従って制御の各段階を逐次進めていく制御」

プラントの出⼒値(測定値)を出⼒し、それをフィードバックさせ、⼊⼒値と出⼒値を常に⽐較して両者を⼀致させるように全体の操作量を調整する制御
https://www.altexcorp.co.jp/controlsystem/wocontrolsystem01-0-1-0

Wake‐Mixing

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.

Normal‐operation 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.

Wake‐Mixing<<• 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-Mixing
九州大学・内田研究室での取組)ORZ

加振装置製作し、浮体の動揺(ピッチ運動)を再現• Pitch motion amplitude: ±5 [deg]• Pitch motion period: 0.8 [s]20The power output of downstream WT | Methods•𝝍𝟎°, 𝟑𝟎°

•Pitch motion𝟑𝟎°•

𝝍𝟎°

The power generation performance of the•Fixedwind turbine was evaluated by varying theblade 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]±5

Pitch motion period [s]0.821

The power output of downstream wind turbine•The power generation performance of the windturbine was evaluated by varying the blade
rotation speed.

•Torque was measured and power output was𝑷computed.
𝑻𝒓 𝝎𝑃 W
𝑇𝑟 Nm
𝜔 rad/s

4.6% increase

︓ Power︓ Torque︓ Angular velocity

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=6s

2022年

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.
●

風力エネルギー科学における壮大な課題の●中核要素として特定されている．

1 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 Quasi‐steady flow effects
2.1.1 Axial‐induction‐based control
2.1.2 Wake steering using yaw offsets
2.2 Wake dynamics and turbulence
2.2.1 Wake dynamics
2.2.2 Boundary‐layer turbulence
2.3 Mesoscale effects, blockage and wind farm wakes
3 Control algorithms●

3.1 Current practice — Open‐loop control
3.2 The closed‐loop paradigm
3.3 Synergies with artificial intelligence and other digitalization
concepts
3.4 Controllability, observability, and sensors●

4 Validation and industrial implementation●●

Overview of wind farm flow control concepts andimportant elements that play a role23

参 考 文 献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.
※学際的な課題

4.1 Proof‐of‐concept studies in high‐fidelity 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 co‐design (CCD)
5.2.1 Wind farm CCD for AEP and LCoE objectives
5.2.2 Wind farm CCD for profitability objectives
5.2.3 Wind farm CCD for non‐economic objectives
6 Conclusions