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In this study, an in-depth analysis of commonly used stationary covariance functions is presented in which wind turbine power curve used where GP based power curve has been constructed using different stationary covariance functions, and after that, a comparative analysis has been carried out in order to identify the most effective covariance function. The commonly used squared exponential covariance function is taken as the benchmark, against which other covariance functions are assessed.
The results show that the performance (in terms of model accuracy and uncertainty) of GP fitted power curve models based on rational quadratic covariance functions is almost the same as for the most commonly used squared exponential function. Thus, rational quadratic covariance functions can be used instead of squared exponential covariance functions. In this paper, strength and weakness of stationary covariance functions would be highlighted for effective condition monitoring.
- gaussian process
- wind turbine
- SCADA analysis
- power curve
- machine learning
- covariance function
FingerprintDive into the research topics of 'Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy'. Together they form a unique fingerprint.
Pandit, R. K., Infield, D. & Kolios, A., 8 Jul 2019, In: IET Renewable Power Generation. 13, 9, p. 1503-1510 8 p.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile
Pandit, R. K. & Infield, D., 15 Mar 2019, In: The Journal of Engineering. p. 1-5 5 p.
Research output: Contribution to journal › Conference Contribution › peer-reviewOpen AccessFile16 Downloads (Pure)
Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoringPandit, R. & Infield, D., 13 Dec 2018, 2018 53rd International Universities Power Engineering Conference (UPEC). Piscataway, NJ: IEEE, 6 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution bookOpen AccessFile
Pandit, R. (Recipient), 18 Jan 2016
Prize: Fellowship awarded competitivelyFile