Wind turbine condition assessment through power curve copula modeling

Research output: Contribution to journalArticle

106 Citations (Scopus)

Abstract

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw and pitch errors.
LanguageEnglish
Pages94-101
Number of pages8
JournalIEEE Transactions on Sustainable Energy
Volume3
Issue number1
DOIs
Publication statusPublished - 1 Jan 2012

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Wind turbines
Condition monitoring
Probability distributions
Distribution functions
Turbines
Degradation

Keywords

  • wind power generation
  • energy conversion
  • power generation reliability

Cite this

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Wind turbine condition assessment through power curve copula modeling. / Gill, Simon; Stephen, Bruce; Galloway, Stuart.

In: IEEE Transactions on Sustainable Energy, Vol. 3, No. 1, 01.01.2012, p. 94-101.

Research output: Contribution to journalArticle

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