Picture of smart phone

Open Access research that is better understanding human-computer interaction...

Strathprints makes available scholarly Open Access content by researchers in the Department of Computer & Information Sciences, including those researching information retrieval, information behaviour, user behaviour and ubiquitous computing.

The Department of Computer & Information Sciences hosts The Mobiquitous Lab, which investigates user behaviour on mobile devices and emerging ubiquitous computing paradigms. The Strathclyde iSchool Research Group specialises in understanding how people search for information and explores interactive search tools that support their information seeking and retrieval tasks, this also includes research into information behaviour and engagement.

Explore the Open Access research of The Mobiquitous Lab and the iSchool, or theDepartment of Computer & Information Sciences more generally. Or explore all of Strathclyde's Open Access research...

Wind turbine Cpmax and drivetrain-losses estimation using Gaussian process machine learning

Hart, E and Leithead, W E and Feuchtwang, J (2018) Wind turbine Cpmax and drivetrain-losses estimation using Gaussian process machine learning. Journal of Physics: Conference Series, 1037. ISSN 1742-6588

Text (Hart-etal-JPCS-2018-Wind-turbine-Cpmax-and-drivetrain-losses-estimation)
Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (296kB) | Preview


In this paper it is shown that measured data in a wind turbine, available to the controller, can be formulated into a polynomial regression problem in order to estimate the turbine's maximum efficiency power coefficient, Cpmax, and drivetrain losses, assuming the latter can be well approximated as being linear. Gaussian process (GP) machine learning is used for the regression problem. These formulations are tested on data generated using the Supergen Exemplar 5 MW wind turbine model, with results indicating that this is a potential low cost method for detecting changes in aerodynamic efficiency and drivetrain losses. The GP approach is benchmarked against standard least-squares (LS) regression, with the GP shown to be the superior method in this case.