Chaired by: Morten Hartvig Hansen | University of Southern Denmark
Topic: MST. Modeling and Simulation Technology
Form of presentation: Oral
Duration: 90 minutes
Stefan Kock, Georg Jacobs, Dennis Bosse, Avinash Sharma
The most existing multi-MW nacelle test benches (NTB) measure the MN·m torque before load application system (LAS) to reduce the cross-talk effect of the multi-component forces and bending moments on torque measurement. This means that the friction torque of the LAS reduces the applied torque and consequently directly determines the input torque on the wind turbine under test (WUT). Therefore the knowledge of the friction torque is necessary for the precise experimental investigations (e.g. efficiency measurement). At the beginning of this paper different simulative methods for the determination of the friction torque of the LAS, which are suspended by hydrostatical plain bearings, are introduced. After the description and metrological validation of the introduced methods, the best suitable simulative method is selected regarding the effort for the model creation, computational time and result accuracy. Afterwards the friction torque is quantified under different operation conditions (e.g. variation of rotational speed, multi-component load and temperature). Finally, the influence of the quantified friction torque on the uncertainty of the MN·m measurement is compiled.
Riccardo Riva, Stefano Cacciola, Alessandro Croce
The stability analysis of in-operation wind turbines is a very important topic, that has received considerable attention in the last years. Many identification algorithms have been developed to estimate frequencies and damping ratios, but very few papers have been dedicated to the mode shapes. The knowledge of high-resolution mode shapes could be exploited for several applications including model validation, accurate description of the vibratory content of a machine and spatially-accurate damage detection. In this work, we will present a procedure to compute the high-resolution periodic mode shapes of a wind turbine, and apply it to a high-fidelity wind turbine model. The results show that this methodology is able to identify the first low-damped modes of the system with good accuracy.
Maik Reder, Julio J Melero
With the growing wind energy sector, the need for advanced operation and maintenance (O&M) strategies has emerged. So far, mainly corrective or preventive O&M actions are applied. Predictive modelling, however, is expected to significantly enhance existing O&M practice. Here, anticipating wind turbine component failures can enable operators to lower the O&M cost and is particularly useful for wind farms located in remote areas or offshore locations. Previous research has shown that the failure behaviour of wind turbines and their components is highly influenced by the meteorological conditions under which the turbines operate. Hence, there is a significant need for robust models for failure prediction taking into consideration these conditions. Furthermore, solutions need to be found in order to determine the most suitable input variables for enhancing their prediction accuracy.This study uses failure data obtained from 984 wind turbines during 87 operational WT years. Bayesian belief networks (BBN) are trained based on failure records, technology specific covariates, as well as measurements of the environmental and operational conditions at site. Subsequently, the failure events in a wind farm during a period of 36 months are predicted with the BNN, whereas the failure events of six main components are predicted separately. Furthermore, an extensive sensitivity study is carried out to find the model with the highest prediction accuracy for each component. The influence of each meteorological, operational or technical covariate are discussed in detail. The models achieved a very good accuracy and were able to predict the majority of the component failures over the prediction period.
In the present study the actuator line method is used to simulate the wake of a wind turbine and the resulting impact on the flight of a helicopter through the wake. The helicopter within the simulation setup is represented by a dynamic actuator line which was implemented into the flow solver FLOWer. In order to evaluate the loads on the helicopter different parameters are compared including force distribution and the angle of attack in different areas of the wake. For further improvement of the actuator line implementation three different methods to evaluate the angle of attack are compared. This was examined as the angle of attack is a highly importantparameter for the actuator line method and helps to optimise the performance of the actuator line method as well as to reduce the sensitivity of the result on input parameters. As there is no experimental data available and the model of the helicopter comes with distinct simplifications the loads are compared to values without the wake of the wind turbine thus in free flight.