Search

The outlined analysis validates the dynamic wake meandering (DWM) model based on loads and power production measured at an onshore wind farm with small turbine distances. Special focus is given to the performance of a version of the DWM model that was previously recalibrated at the site. The recalibration is based on measurements from a turbine nacelle-mounted lidar system. The different versions of the DWM model are compared to the commonly used Frandsen wake-added turbulence model. The results of the recalibrated wake model agree very well with the measurements, whereas the Frandsen model overestimates the loads drastically for short turbine distances. Furthermore, lidar measurements of the wind speed deficit as well as the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.

Light detection and ranging (lidar) systems have gained a great importance in today's wake characteristic measurements. The aim of this measurement campaign is to track the wake meandering and in a further step to validate the wind speed deficit in the meandering frame of reference (MFR) and in the fixed frame of reference using nacelle-mounted lidar measurements. Additionally, a comparison of the measured and the modeled wake degradation in the MFR was conducted. The simulations were done with two different versions of the dynamic wake meandering (DWM) model. These versions differ only in the description of the quasi-steady wake deficit. Based on the findings from the lidar measurements, the impact of the ambient turbulence intensity on the eddy viscosity definition in the quasi-steady deficit has been investigated and, subsequently, an improved correlation function has been determined, resulting in very good conformity between the new model and the measurements.

Light Detection And Ranging (LiDAR) systems have gained a great importance in today's wake characteristic measurements. The aim of this measurement campaign is to track the wake meandering and in a further step to validate the wind speed deficit in the meandering frame of reference (MFR) and in the fixed frame of reference using nacelle mounted LiDAR measurements. The measurement campaign has been prepared in detail by preliminary simulations mimicking the LiDAR behavior and corresponding wind field simulations. Additionally, a comparison between the modelled wake degradation in the MFR and the measured one could be conducted. The simulations were done with two different versions of the Dynamic Wake Meandering (DWM) model. These versions differ only in the description of the quasi-steady wake deficit. Based on the findings from the LiDAR measurements the formulation of the quasi-steady wake deficit in the DWM model has been adjusted, so that the recalibrated model coincides very well with the measurements.

This paper examines the applicability of piezoelectric patch transducers as condition monitoring sensors for the drive train of wind turbines. Three laboratory experiments are conducted to determine the sensor’s temperature stability, sensitivity toward electromagnetic interference and general ability to detect bearing damages. The sensors show little deviation under thermal influence in a tested temperature range between -40 °C and +80 °C. Although they react to electromagnetic interference, the disturbance signal is predominated by the damage signal. Proper shielding is yet recommended to fully eliminate electromagnetic interference. An example of shielding is given through the use of mu-metal. In direct comparison of bearing fault detection capability to a state-of-the-art accelerometer, the patch transducer performs slightly worse in matters of signal-to-noise-ratio. However, when excited by small damage impulses, it yields good signal depth and output. Altogether, the sensor shows fundamental applicability as a condition monitoring sensor in all experiments, though further optimization seems feasible and worthwhile.