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Abstract
Unscheduled maintenance consumes a lot of time and effort and hence reduces the overall cost-effectiveness of wind turbines. Supervisory control and data acquisition (SCADA) based condition monitoring is a cost-effective approach to carry out diagnosis and prognosis of faults and to provide performance assessment of a wind turbine. The rotor speed based power curve, which describes the nonlinear relationship between wind turbine rotor speed and power output, is useful for performance appraisal of a wind turbine though limited work on this area has been undertaken to date. Support Vector Machine (SVM) is a data-driven, nonparametric approach used for both classification and regression problems developed initially from statistical learning theory (SLT) by Vapnik. SVM is useful in forecasting and prediction applications.
This paper deals with the application of support vector regression to estimate the rotor speed based power curve of a wind turbine and its usefulness in identifying potential faults. It is compared with a conventional approach based on a binned rotor speed power curve to identify operational anomalies. The comparative studies summaries the advantages and disadvantages of these techniques. SCADA data obtained from a healthy operational wind turbine is used to train and validate these methods.
This paper deals with the application of support vector regression to estimate the rotor speed based power curve of a wind turbine and its usefulness in identifying potential faults. It is compared with a conventional approach based on a binned rotor speed power curve to identify operational anomalies. The comparative studies summaries the advantages and disadvantages of these techniques. SCADA data obtained from a healthy operational wind turbine is used to train and validate these methods.
Original language | English |
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Title of host publication | 2018 53rd International Universities Power Engineering Conference (UPEC) |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 9781538629109 |
DOIs | |
Publication status | Published - 13 Dec 2018 |
Event | The 53rd International Universities Power Engineering Conference - Glasgow Caledonian University, Glasgow , United Kingdom Duration: 4 Sept 2018 → 7 Sept 2018 Conference number: 53 http://www.upec2018.com/ |
Conference
Conference | The 53rd International Universities Power Engineering Conference |
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Abbreviated title | UPEC2018 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 4/09/18 → 7/09/18 |
Internet address |
Keywords
- wind turbine
- condition monitoring
- SCADA analysis
- predictive maintenance
- SVM technique
- rotor speed
Fingerprint
Dive into the research topics of 'Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in 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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Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy
Pandit, R. K. & Infield, D., 30 Sept 2019, In: Renewable Energy. 140, p. 190-202 13 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile34 Citations (Scopus)47 Downloads (Pure) -
Comparison of advanced non-parametric models for wind turbine power curves
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-review
Open AccessFile40 Citations (Scopus)109 Downloads (Pure) -
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
Prizes
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Marie Curie Early stage reseacher
Pandit, R. (Recipient), 18 Jan 2016
Prize: Fellowship awarded competitively
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