Projekter pr. år
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.
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.
| Originalsprog | Engelsk |
|---|---|
| Titel | 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023 |
| Antal sider | 6 |
| Forlag | IEEE |
| Publikationsdato | aug. 2023 |
| ISBN (Trykt) | 979-8-3503-2070-1 |
| ISBN (Elektronisk) | 979-8-3503-2069-5 |
| DOI | |
| Status | Udgivet - aug. 2023 |
| Begivenhed | 19th International Conference on Automation Science and Engineering (CASE) - Auckland, New Zealand Varighed: 26 aug. 2023 → 30 aug. 2023 |
Konference
| Konference | 19th International Conference on Automation Science and Engineering (CASE) |
|---|---|
| Land/Område | New Zealand |
| By | Auckland |
| Periode | 26/08/2023 → 30/08/2023 |
| Navn | IEEE International Conference on Automation Science and Engineering |
|---|---|
| ISSN | 2161-8070 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Wind Farm Power Production and Fatigue Load Optimization Through Wake Steering'. Sammen danner de et unikt fingeraftryk.-
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DATA-BASED WIND FARM MODELING AND CONTROL
Miao, Y., N. Soltani, M. & Hajizadeh, A.
04/09/2020 → 31/08/2024
Projekter: Projekt › Ph.d.-projekt