Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring
Pandit, Ravi Kumar and Infield, David (2018) Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring. International Journal of Energy and Environmental Engineering. pp. 1-8. ISSN 2008-9163 (https://doi.org/10.1007/s40095-018-0287-3)
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Abstract
The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. In several studies, the wind turbine power curve has been used as a critical indicator for power performance assessment. In contrast, the application of the blade pitch angle curve has hardly been explored for wind turbine condition monitoring purposes. The blade pitch angle curve describes the nonlinear relationship between pitch angle and hub height wind speed and can be used for the detection of faults. A support vector machine (SVM) is an improved version of an artificial neural networks (ANN) and is widely used for classification- and regression-related problems. Support vector regression is a data-driven approach based on statistical learning theory and a structural risk minimization principle which provides useful nonlinear system modeling. In this paper, a support vector regression (a nonparametric machine learning approach)-based pitch curve is presented and its application to anomaly detection explored for wind turbine condition monitoring. A radial basis function (RBF) was used as the kernel function for effective SVR blade pitch curve modeling. This approach is then compared with a binned pitch curve in the identification of operational anomalies. The paper will outline the advantages and limitations 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: 65760 Dates: DateEvent12 October 2018Published12 October 2018Published Online1 October 2018AcceptedSubjects: Technology > Engineering (General). Civil engineering (General) > Environmental engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 12 Oct 2018 10:32 Last modified: 30 Sep 2024 00:26 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65760