Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals

Pandit, Ravi Kumar and Infield, David (2017) Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. In: 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, Piscataway, N.J., pp. 744-749. ISBN 978-1-5090-4683-6

[img]
Preview
Text (Pandit-Infield-ICCEP-2017-Using-Gaussian-process-theory-for-wind-turbine-power)
Pandit_Infield_ICCEP_2017_Using_Gaussian_process_theory_for_wind_turbine_power.pdf
Accepted Author Manuscript

Download (805kB)| Preview

    Abstract

    High operation and maintenance (O&M) costs may affect the profitability and growth of wind turbine industries in long term, especially where offshore wind farms are concerned. With the increase in age of wind turbines and the expansion of offshore wind, the operation and maintenance (O&M) cost is expected to grow significantly which reinforces the drive towards condition based maintenance. Wind turbine power curves play a central role in the assessment of turbine operational health. Gaussian process theory is finding increasing application in this current emerging research area. This paper investigates the potential of Gaussian process models to improve the representation of wind turbine power curves and in particular the importance of confidence intervals as determined by such modeling.