Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering

Yizhi Miao; Mohsen N. Soltani; Amin Hajizadeh

doi:10.1109/CASE56687.2023.10260632

Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering

Yizhi Miao, Mohsen N. Soltani, Amin Hajizadeh

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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Abstract

Wake steering has proven potential to increase
wind farm production. However, this control strategy prioritizes
the maximum power without considering the effects of fatigue
load, a remaining life indicator on a wind turbine component.
Reducing fatigue loads is one of the most important goals
of wind farm control, especially for long-established wind
farms. Therefore, it is crucial to consider the fatigue load
when optimizing the yaw angles. This paper proposes a multiobjective
wake steering control strategy to balance power
production and fatigue load. Firstly, a power production model
based on Gauss–Curl Hybrid (GCH) wake model is introduced
to estimate the farm power. Secondly, a fatigue load prediction
model is proposed based on Gaussian Process Regression
(GPR). Finally, the Particle Swarm Optimization (PSO) method
is adopted to optimize the yaw offset by considering the tradeoff
between power production and fatigue load. The proposed
balancing strategy shows an increase in power generation by
16% compared to the baseline strategy (Greedy strategy) while
reducing the average and maximum fatigue load by 12% and
10%, respectively.

Original language	English
Title of host publication	2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
Number of pages	6
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	Aug 2023
ISBN (Print)	979-8-3503-2070-1
ISBN (Electronic)	979-8-3503-2069-5
DOIs	https://doi.org/10.1109/CASE56687.2023.10260632
Publication status	Published - Aug 2023
Event	19th International Conference on Automation Science and Engineering (CASE) - Auckland, New Zealand Duration: 26 Aug 2023 → 30 Aug 2023

Conference

Conference	19th International Conference on Automation Science and Engineering (CASE)
Country/Territory	New Zealand
City	Auckland
Period	26/08/2023 → 30/08/2023

Series	IEEE International Conference on Automation Science and Engineering
ISSN	2161-8070

Keywords

Wind Farm
Flow Control
Yaw control
Power
Load reduction

Access to Document

10.1109/CASE56687.2023.10260632

CASE2023 Wind Farm Power Production and Fatigue Load Optimization Through Wake SteeringAccepted author manuscript, 755 KBLicence: CC BY 4.0

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Cite this

@inproceedings{f0f8467064444df388ce52880daa29a6,

title = "Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering",

abstract = "Wake steering has proven potential to increasewind farm production. However, this control strategy prioritizesthe maximum power without considering the effects of fatigueload, a remaining life indicator on a wind turbine component.Reducing fatigue loads is one of the most important goalsof wind farm control, especially for long-established windfarms. Therefore, it is crucial to consider the fatigue loadwhen optimizing the yaw angles. This paper proposes a multiobjectivewake steering control strategy to balance powerproduction and fatigue load. Firstly, a power production modelbased on Gauss–Curl Hybrid (GCH) wake model is introducedto estimate the farm power. Secondly, a fatigue load predictionmodel is proposed based on Gaussian Process Regression(GPR). Finally, the Particle Swarm Optimization (PSO) methodis adopted to optimize the yaw offset by considering the tradeoffbetween power production and fatigue load. The proposedbalancing strategy shows an increase in power generation by16% compared to the baseline strategy (Greedy strategy) whilereducing the average and maximum fatigue load by 12% and10%, respectively.",

keywords = "Wind Farm, Flow Control, Yaw control, Power, Load reduction",

author = "Yizhi Miao and {N. Soltani}, Mohsen and Amin Hajizadeh",

year = "2023",

month = aug,

doi = "10.1109/CASE56687.2023.10260632",

language = "English",

isbn = "979-8-3503-2070-1",

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publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

booktitle = "2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023",

address = "United States",

note = "19th International Conference on Automation Science and Engineering (CASE) ; Conference date: 26-08-2023 Through 30-08-2023",

}

Miao, Y , N. Soltani, M & Hajizadeh, A 2023, Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering. in 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE (Institute of Electrical and Electronics Engineers), IEEE International Conference on Automation Science and Engineering, 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 26/08/2023. https://doi.org/10.1109/CASE56687.2023.10260632

Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering. / Miao, Yizhi ; N. Soltani, Mohsen ; Hajizadeh, Amin.
2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE (Institute of Electrical and Electronics Engineers), 2023. (IEEE International Conference on Automation Science and Engineering).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering

AU - Miao, Yizhi

AU - N. Soltani, Mohsen

AU - Hajizadeh, Amin

PY - 2023/8

Y1 - 2023/8

N2 - Wake steering has proven potential to increasewind farm production. However, this control strategy prioritizesthe maximum power without considering the effects of fatigueload, a remaining life indicator on a wind turbine component.Reducing fatigue loads is one of the most important goalsof wind farm control, especially for long-established windfarms. Therefore, it is crucial to consider the fatigue loadwhen optimizing the yaw angles. This paper proposes a multiobjectivewake steering control strategy to balance powerproduction and fatigue load. Firstly, a power production modelbased on Gauss–Curl Hybrid (GCH) wake model is introducedto estimate the farm power. Secondly, a fatigue load predictionmodel is proposed based on Gaussian Process Regression(GPR). Finally, the Particle Swarm Optimization (PSO) methodis adopted to optimize the yaw offset by considering the tradeoffbetween power production and fatigue load. The proposedbalancing strategy shows an increase in power generation by16% compared to the baseline strategy (Greedy strategy) whilereducing the average and maximum fatigue load by 12% and10%, respectively.

AB - Wake steering has proven potential to increasewind farm production. However, this control strategy prioritizesthe maximum power without considering the effects of fatigueload, a remaining life indicator on a wind turbine component.Reducing fatigue loads is one of the most important goalsof wind farm control, especially for long-established windfarms. Therefore, it is crucial to consider the fatigue loadwhen optimizing the yaw angles. This paper proposes a multiobjectivewake steering control strategy to balance powerproduction and fatigue load. Firstly, a power production modelbased on Gauss–Curl Hybrid (GCH) wake model is introducedto estimate the farm power. Secondly, a fatigue load predictionmodel is proposed based on Gaussian Process Regression(GPR). Finally, the Particle Swarm Optimization (PSO) methodis adopted to optimize the yaw offset by considering the tradeoffbetween power production and fatigue load. The proposedbalancing strategy shows an increase in power generation by16% compared to the baseline strategy (Greedy strategy) whilereducing the average and maximum fatigue load by 12% and10%, respectively.

KW - Wind Farm

KW - Flow Control

KW - Yaw control

KW - Power

KW - Load reduction

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U2 - 10.1109/CASE56687.2023.10260632

DO - 10.1109/CASE56687.2023.10260632

M3 - Article in proceeding

SN - 979-8-3503-2070-1

T3 - IEEE International Conference on Automation Science and Engineering

BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023

PB - IEEE (Institute of Electrical and Electronics Engineers)

T2 - 19th International Conference on Automation Science and Engineering (CASE)

Y2 - 26 August 2023 through 30 August 2023

ER -

Miao Y , N. Soltani M , Hajizadeh A. Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering. In 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE (Institute of Electrical and Electronics Engineers). 2023. (IEEE International Conference on Automation Science and Engineering). doi: 10.1109/CASE56687.2023.10260632

Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering

Abstract

Conference

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

IEA Wind Task 44: Farm Flow Control

DATA-BASED WIND FARM MODELING AND CONTROL

Offshore Wind and Wave Laboratory

Cite this

Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering

Abstract

Conference

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

IEA Wind Task 44: Farm Flow Control

DATA-BASED WIND FARM MODELING AND CONTROL

Equipment

Offshore Wind and Wave Laboratory

Cite this