Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring
Pandit, Ravi Kumar and Infield, David (2018) Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring. Journal of Physics: Conference Series, 1102 (1). 012037. ISSN 1742-6588 (https://doi.org/10.1088/1742-6596/1102/1/012037)
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Abstract
Several studies have used the power curve as a critical indicator to assess the performance of wind turbines. However, the wind turbine internal operation is affected by various parameters, particularly by blade pitch angle. Continuous monitoring of blade pitch angle can be useful for power performance assessment of wind turbines. The blade pitch curve describes the nonlinear relationship between pitch angle and hub height wind speed which to date has been little explored for wind turbine condition monitoring. Gaussian Process models are nonlinear and nonparametric technique, based on Bayesian probability theory. Such models have the potential give results quickly and efficiently. In this paper, we propose a Gaussian Process model to predict blade pitch curve of a wind turbine for condition monitoring purposes. The obtained Gaussian Process based blade pitch curve is then compared with a conventional approach based on a binned blade pitch curve for identifying operational anomalies purposes. Finally, the weaknesses and strengths of these methods are summarised. SCADA data from healthy wind turbines are used to train and evaluate the performance of these techniques.
ORCID iDs
Pandit, Ravi Kumar ORCID: https://orcid.org/0000-0001-6850-7922 and Infield, David;-
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Item type: Article ID code: 65809 Dates: DateEvent10 October 2018Published15 April 2018AcceptedSubjects: Science > Physics
Technology > Electrical engineering. Electronics Nuclear engineeringDepartment: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 16 Oct 2018 12:28 Last modified: 11 Nov 2024 12:08 URI: https://strathprints.strath.ac.uk/id/eprint/65809