Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

Abstract

High operation and maintenance (O&M) costs may affect the profitability and growth of wind turbine industries in long term, especially where offshore wind farms are concerned. With the increase in age of wind turbines and the expansion of offshore wind, the operation and maintenance (O&M) cost is expected to grow significantly which reinforces the drive towards condition based maintenance. Wind turbine power curves play a central role in the assessment of turbine operational health. Gaussian process theory is finding increasing application in this current emerging research area. This paper investigates the potential of Gaussian process models to improve the representation of wind turbine power curves and in particular the importance of confidence intervals as determined by such modeling.
LanguageEnglish
Title of host publication2017 6th International Conference on Clean Electrical Power (ICCEP)
Place of PublicationPiscataway, N.J.
PublisherIEEE
Pages744-749
Number of pages6
ISBN (Print)978-1-5090-4683-6
DOIs
Publication statusPublished - 18 Aug 2017
Event6th International Conference on CLEAN ELECTRICAL POWER Renewable Energy Resources Impact - Santa Margherita Ligure, Liguria, Italy
Duration: 27 Jun 201729 Jun 2017
http://www.iccep.net/

Conference

Conference6th International Conference on CLEAN ELECTRICAL POWER Renewable Energy Resources Impact
CountryItaly
City Liguria
Period27/06/1729/06/17
Internet address

Fingerprint

Wind turbines
Offshore wind farms
Costs
Profitability
Turbines
Health
Industry

Keywords

  • wind turbine
  • gaussian process models
  • condition monitoring
  • SCADA data
  • power curve

Cite this

Pandit, R. K., & Infield, D. (2017). Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. In 2017 6th International Conference on Clean Electrical Power (ICCEP) (pp. 744-749). Piscataway, N.J.: IEEE. https://doi.org/10.1109/ICCEP.2017.8004774
Pandit, Ravi Kumar ; Infield, David. / Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. 2017 6th International Conference on Clean Electrical Power (ICCEP). Piscataway, N.J. : IEEE, 2017. pp. 744-749
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abstract = "High operation and maintenance (O&M) costs may affect the profitability and growth of wind turbine industries in long term, especially where offshore wind farms are concerned. With the increase in age of wind turbines and the expansion of offshore wind, the operation and maintenance (O&M) cost is expected to grow significantly which reinforces the drive towards condition based maintenance. Wind turbine power curves play a central role in the assessment of turbine operational health. Gaussian process theory is finding increasing application in this current emerging research area. This paper investigates the potential of Gaussian process models to improve the representation of wind turbine power curves and in particular the importance of confidence intervals as determined by such modeling.",
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Pandit, RK & Infield, D 2017, Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. in 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, Piscataway, N.J., pp. 744-749, 6th International Conference on CLEAN ELECTRICAL POWER Renewable Energy Resources Impact, Liguria, Italy, 27/06/17. https://doi.org/10.1109/ICCEP.2017.8004774

Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. / Pandit, Ravi Kumar; Infield, David.

2017 6th International Conference on Clean Electrical Power (ICCEP). Piscataway, N.J. : IEEE, 2017. p. 744-749.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Pandit RK, Infield D. Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. In 2017 6th International Conference on Clean Electrical Power (ICCEP). Piscataway, N.J.: IEEE. 2017. p. 744-749 https://doi.org/10.1109/ICCEP.2017.8004774