Analysis of measurements on atmospheric turbulence with respect to the statistics of velocity increments reveals that the statistics are not Gaussian but highly intermittent. Here, we demonstrate that the higher quantity of extreme events in atmospheric wind fi elds transfers to alternating loads on the airfoil and on the main shaft in the form of torque fl uctuations. For this purpose, alternating loads are discussed with respect to their increment statistics. Our conjecture is that the anomalous wind statistics are responsible for load changes, which may potentially contribute to additional loads and may cause additional fatigue. Our analysis is performed on three different wind fi eld data sets: measured fi elds, data generated by a standard wind fi eld model and data generated by an alternative model based on continuous time random walks, which grasps the intermittent structure of atmospheric turbulence in a better way. Our fi ndings suggest that fl uctuations in the loads might not be refl ected properly by the standard wind fi eld models.

Wind turbines operate in the atmospheric boundary layer, where they are exposed to the turbulent atmospheric flows. As the response time of wind turbine is typically in the range of seconds, they are affected by the small scale intermittent properties of the turbulent wind. Consequently, basic features which are known for small-scale homogeneous isotropic turbulence, and in particular the well-known intermittency problem, have an important impact on the wind energy conversion process. We report on basic research results concerning the small-scale intermittent properties of atmospheric flows and their impact on the wind energy conversion process. The analysis of wind data shows strongly intermittent statistics of wind fluctuations. To achieve numerical modeling a data-driven superposition model is proposed. For the experimental reproduction and adjustment of intermittent flows a so-called active grid setup is presented. Its ability is shown to generate reproducible properties of atmospheric flows on the smaller scales of the laboratory conditions of a wind tunnel. As an application example the response dynamics of different anemometer types are tested. To achieve a proper understanding of the impact of intermittent turbulent inflow properties on wind turbines we present methods of numerical and stochastic modeling, and compare the results to measurement data. As a summarizing result we find that atmospheric turbulence imposes its intermittent features on the complete wind energy conversion process. Intermittent turbulence features are not only present in atmospheric wind, but are also dominant in the loads on the turbine, i.e. rotor torque and thrust, and in the electrical power output signal. We conclude that profound knowledge of turbulent statistics and the application of suitable numerical as well as experimental methods are necessary to grasp these unique features and quantify their effects on all stages of wind energy conversion.

The power performance of a wind energy converter (WEC) commonly refers to the relation between the input source and the electrical output, i.e. the input wind speed u and the electrical power output P . The International Electrotechnical Commission defined a so-called power curve P .u/ that quantifies this relation. Recently, a novel approach was introduced based on the short-time dynamical response of the WEC to high-frequency wind fluctuations. The dynamical behavior of the WEC is quantified by a drift field and the corresponding Langevin power curve (LPC).We present three applications of our method to wind energy based on the LPC. The first application consists of testing the power performance of WECs using LIDAR wind measurements. We then extend this test to the monitoring of the WEC performance over time. Finally, we apply the LPC to a simulation model for a WEC as a tool to characterize its performance. These applications illustrate the flexibility of the LPC as a relevant tool for performance testing and monitoring.

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 get 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.

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