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

Georgios Gasparis, Wai Hou Lio, Fanzhong Meng*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

33 Downloads (Pure)


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.
Original languageEnglish
Article number6360
Issue number23
Number of pages15
Publication statusPublished - 2020


  • Surrogate model
  • Fatigue load
  • Wind turbine
  • Wind farm

Fingerprint Dive into the research topics of 'Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm'. Together they form a unique fingerprint.

Cite this