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Based on the Langevin equation, it has been proposed to obtain power curves for wind turbines from high‐frequency data of wind speed measurements  u ( t ) and power output  P ( t ). The two parts of the Langevin approach, power curve and drift field, give a comprehensive description of the conversion dynamic over the whole operating range of the wind turbine. The method deals with high‐ frequent data instead of 10 min means. It is therefore possible to gain a reliable power curve already from a small amount of data per wind speed. Furthermore, the method is able to visualize multiple fixed points, which is, e.g. characteristic for the transition from partial to full load or in case the conversion process deviates from the standard procedures. In order to gain a deeper knowledge, it is essential that the method works not only for measured data but also for numerical wind turbine models and synthetic wind fields. Here, we characterize the dynamics of a detailed numerical wind turbine model and calculate the Langevin power curve for different data samplings. We show how to obtain reliable results from synthetic data and verify the applicability of the method for field measurements with ultra‐sonic, cup, and Lidar measurements. The independence of the fixed points on site‐ specific turbulence effects is also confirmed with the numerical model. Furthermore, we demonstrate the potential of the Langevin approach to detect failures in the conversion process and thus show the potential of the Langevin approach for a condition monitoring system. Copyright © 2014 John Wiley & Sons, Ltd.

Langevin power curve analysis for numerical wind energy converter models with new insights on high frequency power performance