LAPSE:2023.27976
Published Article
LAPSE:2023.27976
Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm
April 11, 2023
Fatigue damage of turbine components is typically computed by running a rain-flow counting algorithm on the load signals of the components. This process is not linear and time consuming, thus, it is non-trivial for an application of wind farm control design and optimisation. To compensate this limitation, this paper will develop and compare different types of surrogate models that can predict the short term damage equivalent loads and electrical power of wind turbines, with respect to various wind conditions and down regulation set-points, in a wind farm. More specifically, Linear Regression, Artificial Neural Network and Gaussian Process Regression are the types of the developed surrogate models in this work. The results showed that Gaussian Process Regression outperforms the other types of surrogate models and can effectively estimate the aforementioned target variables.
Record ID
Keywords
fatigue load, Surrogate Model, wind farm, wind turbine
Subject
Suggested Citation
Gasparis G, Lio WH, Meng F. Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm. (2023). LAPSE:2023.27976
Author Affiliations
Journal Name
Energies
Volume
13
Issue
23
Article Number
E6360
Year
2020
Publication Date
2020-12-02
Published Version
ISSN
1996-1073
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Original Submission
Other Meta
PII: en13236360, Publication Type: Journal Article
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Published Article
LAPSE:2023.27976
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doi:10.3390/en13236360
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[v1] (Original Submission)
Apr 11, 2023
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Apr 11, 2023
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