Abstract
| Language | English |
|---|---|
| Pages | 94-101 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Sustainable Energy |
| Volume | 3 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2012 |
Fingerprint
Keywords
- wind power generation
- energy conversion
- power generation reliability
Cite this
}
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 journal › Article
TY - JOUR
T1 - Wind turbine condition assessment through power curve copula modeling
AU - Gill, Simon
AU - Stephen, Bruce
AU - Galloway, Stuart
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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.
AB - 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.
KW - wind power generation
KW - energy conversion
KW - power generation reliability
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6102291
U2 - 10.1109/TSTE.2011.2167164
DO - 10.1109/TSTE.2011.2167164
M3 - Article
VL - 3
SP - 94
EP - 101
JO - IEEE Transactions on Sustainable Energy
T2 - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
SN - 1949-3029
IS - 1
ER -