In the wind industry, it is important to assess a turbine systems response under different wind profiles. For instance, a wind-to-power relationship is crucial for wind power forecast, and a wind-to-stress relationship is important for selecting critical design parameters meeting the reliability requirement. Given the complexity involved in a turbine system, it is impossible to write a neat, analytical expression to underlie the abovementioned relationships. Almost invariably does the wind industry resort to data driven methods for a solution, namely that wind data and the corresponding turbine response data (bending moments or power outputs) are used together to fit empirically the functional relationship of interest. This paper presents a couple of nonparametric data analytic methods relevant to wind energy applications with real life example for demonstration.
|Original language||English (US)|
|Title of host publication||Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy|
|Publisher||American Society of Mechanical Engineers|
|State||Published - Aug 12 2015|
Bibliographical noteKAUST Repository Item: Exported on 2022-06-24
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Ding and Tang were partially supported by the grants from NSF (CMMI-1300560 and CMMI-1300236). Ding and Huang were partially supported by the grant from King Abdullah University of Science and Technology (KUS-CI-016-04).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.