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Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

Author

Listed:
  • Georgios Gasparis

    (Department of Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark)

  • Wai Hou Lio

    (Department of Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark)

  • Fanzhong Meng

    (Department of Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark)

Abstract

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.

Suggested Citation

  • Georgios Gasparis & Wai Hou Lio & Fanzhong Meng, 2020. "Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm," Energies, MDPI, vol. 13(23), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6360-:d:454837
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    References listed on IDEAS

    as
    1. Yingming Liu & Yingwei Wang & Xiaodong Wang & Jiangsheng Zhu & Wai Hou Lio, 2019. "Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load," Energies, MDPI, vol. 12(8), pages 1-23, April.
    2. Christos Galinos & Jonas Kazda & Wai Hou Lio & Gregor Giebel, 2020. "T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case," Energies, MDPI, vol. 13(6), pages 1-16, March.
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    Cited by:

    1. Hesong Cui & Xueping Li & Gongping Wu & Yawei Song & Xiao Liu & Derong Luo, 2021. "MPC Based Coordinated Active and Reactive Power Control Strategy of DFIG Wind Farm with Distributed ESSs," Energies, MDPI, vol. 14(13), pages 1-19, June.
    2. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).

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