Gaussian process power curve models incorporating wind turbine operational variables
Pandit, Ravi Kumar and Infield, David and Kolios, Athanasios (2020) Gaussian process power curve models incorporating wind turbine operational variables. Energy Reports, 6. pp. 1658-1669. ISSN 2352-4847 (https://doi.org/10.1016/j.egyr.2020.06.018)
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Abstract
The IEC standard 61400−12−1 recommends a reliable and repeatable methodology called ‘binning’ for accurate computation of wind turbine power curves that recognise only the mean wind speed at hub height and the air density as relevant input parameters. However, several literature studies have suggested that power production from a wind turbine also depends significantly on several operational variables (such as rotor speed and blade pitch angle) and incorporating these could improve overall accuracy and fault detection capabilities. In this study, a Gaussian Process (GP), a machine learning, data-driven approach, based power curve models that incorporates these operational variables are proposed in order to analyse these variables impact on GP models accuracy as well as uncertainty. This study is significant as it find out key variable that can improve GP based condition monitoring activities (e.g., early failure detection) without additional complexity and computational costs and thus, helps in maintenance decision making process. Historical 10-minute average supervisory control and data acquisition (SCADA) datasets obtained from variable pitch regulated wind turbines, are used to train and validate the proposed research effectiveness The results suggest that incorporating operational variables can improve the GP model accuracy and reduce uncertainty significantly in predicting a power curve. Furthermore, a comparative study shows that the impact of rotor speed on improving GP model accuracy is significant as compared to the blade pitch angle. Performance error metrics and uncertainty calculations are successfully applied to confirm all these conclusions.
ORCID iDs
Pandit, Ravi Kumar ORCID: https://orcid.org/0000-0001-6850-7922, Infield, David and Kolios, Athanasios ORCID: https://orcid.org/0000-0001-6711-641X;-
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Item type: Article ID code: 72973 Dates: DateEvent30 November 2020Published26 June 2020Published Online19 June 2020AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering
Faculty of Engineering > Electronic and Electrical EngineeringDepositing user: Pure Administrator Date deposited: 30 Jun 2020 15:37 Last modified: 27 Nov 2024 07:46 URI: https://strathprints.strath.ac.uk/id/eprint/72973