Projects per year
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.
Original language | English |
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Pages (from-to) | 1-8 |
Number of pages | 8 |
Journal | International Journal of Energy and Environmental Engineering |
Early online date | 12 Oct 2018 |
DOIs | |
Publication status | E-pub ahead of print - 12 Oct 2018 |
Keywords
- condition monitoring
- support vector regression
- performance monitoring
- performance curves
- wind turbine
- SCADA analysis
Fingerprint
Dive into the research topics of 'Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring'. Together they form a unique fingerprint.Projects
- 1 Finished
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AWESOME (H2020 ETN)
Infield, D. (Principal Investigator)
European Commission - Horizon Europe + H2020
1/01/15 → 31/12/18
Project: Research
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Power curve modeling using support vector machine and its accuracy dependence on kernel scale
Pandit, R. & Infield, D., 31 Dec 2018, Fifteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies . Northamption, p. 145-156 12 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
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Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring
Pandit, 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 book
Open AccessFile11 Citations (Scopus)66 Downloads (Pure) -
Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy
Pandit, R. K., Infield, D. & Carroll, J., 22 Oct 2018, In: Wind Energy. p. 1-14 14 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile50 Citations (Scopus)53 Downloads (Pure)