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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.
- wind power generation
- energy conversion
- power generation reliability
1/04/09 → 31/03/13
Zorzi, G., Stephen, B. & McMillan, D., 19 Jun 2018. 1 p.
Research output: Contribution to conference › PosterOpen AccessFile