A stochastic power curve for wind turbines with reduced variability using conditional copula

It has been observed that a large variability exists between wind speed and wind power in real metrological conditions. To reduce this substantial variability, this study developed a stochastic wind turbine power curve by incorporating various exogenous factors. Four measurements, namely, wind azimuth, wind elevation, air density and solar radiation are chosen as exogenous influence factors. A recursive formula based on conditional copulas is used to capture the complex dependency structure between wind speed and wind power with reduced variability. A procedure of selecting a proper form for each factor and its corresponding copula models is given. Through a case study on the small wind turbine located in southeast of Edmonton, Alberta, Canada, we demonstrate that the variability can be reduced significantly by incorporating these influence factors. Wind turbine operators can apply the method reported in this study to construct a stochastic power curve for local wind farms and use it to achieve more accurate power forecasting and health condition monitoring of the turbine. Copyright (c) 2015 John Wiley & Sons, Ltd.