Analysis of wind turbines under harsh operation conditions

This project focuses on the harsh operational conditions that sometimes lead to gearbox and bearing failures before their expected lifespan have been reached. Individual wind turbines that are subjected to extreme wind conditions and other severe operational conditions have been identified in close cooperation with wind turbine operators. Methods for determining the operational severity from data acquired during operation have been developed. Raw measured data have been processed and compared to the assumed data that were used in the wind turbine design process. Feedbacks from maintenance reports have been used to strengthen the relevance of these indices. A generic V90 turbine model has been created for the simulations using both FAST and VIDYN aero elastic simulation codes. The wind turbine dynamical behaviour has been analysed in arbitrary wind conditions and grid dynamics.The assessment has been made using 17 wind turbines of same design but with different operational conditions. The data from these turbines have been compared and comparison has also been made using simulated data for complex and flat terrains. The results show that the complex terrain increases the fatigue on the gearbox shaft and the fluctuation of wind direction in the complex terrain is much higher than for the flat terrain, resulting in more destructive fatigue loads. Simulation also shows that de-rating of the turbine has a significant impact on the loads in the drive train but no significant impact on the fatigue loads on the nacelle.There are different systems running for capturing operational data during operation. Normally the system stores only one measurement data point at each 20 seconds, i.e. 0.05 Hz. Then it is difficult to observe any details related to loads based on turbulence or other environment or technical conditions. For that reason, the project has managed to get higher resolution measurement data, 1 Hz sample rate, for specific turbines in the available fleets.The complex topography of a specific wind farm was extracted from LASer data and it was imported into the Computational Fluid Dynamics (CFD) software, STAR-CCM+, to generate the computational wind grid for the numerical simulations. The on-site meteorology mast data, provided by project partners, were used to determine the dominant mean wind speed/direction and turbulence intensity.A system simulation model in FAST or VIDYN can reasonably well predict the hub forces and mechanical torque of a Vestas V90 turbine under different kind of wind load in operation. A turbine designer is allowed to use any specific wind conditions as input for the load calculations. It might be outside the wind classes specified in IEC-61400-1 and therefore denoted class S, where the specific input conditions are stated. Using the calculated data for the complex terrain, a class 3S turbine has been defined with turbulence intensity, TI=0.17, shear α = 0.34 in comparison with standard wind turbine class 3A with TI=0.16 and α = 0.20. When using these input conditions, a fatigue load comparison and evaluation has been done. It shows that both drive torque and tower top bending moment increase with about 6 percent when class3S is used

Gender Aspects in Driving Style and Its Impact on Battery Ageing

The long and tiring discussion of who are the best drivers, men or women, is not answered in this article. This article, though, sheds some light on the actual differences that can be seen in how men and women drive. In this study, GPS-recorded driving dynamics data from 123 drivers, 48 women and 75 men, are analysed and drivers are categorised as aggressive, normal or gentle. A total of 10% of the drivers was categorised as aggressive, with an even distribution between the genders. For the gentle drivers, 11% of the drivers, the men dominated. The driving style investigation was extended to utilise machine learning, confirming the results from statistical tools. As driving style highly impacts a vehicle\u27s fuel consumption, while switching over to battery electric vehicles it is important to investigate how the different driving styles impact battery utilisation. Two Li-ion battery cell types were tested utilising the same load cycle with three levels of current amplitude, to represent accelerations for the three drive categories. While one cell type was insensitive to the current amplitude, the highly energy-optimised cell proved to be sensitive to higher current amplitudes, corresponding to a more aggressive driving style. Thus, the amplitude of the dynamic current can for some cells be a factor that needs to be considered for lifetime predictions, while it can be neglected for other cells

Optimal preventive maintenance scheduling for wind turbines under condition monitoring

We suggest a mathematical model for computing and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterium takes into account the current ages of the key components, the major maintenance costs including eventual energy production losses as well as the available data monitoring the condition of the wind turbines. We illustrate our approach with a case study based on data collected from several wind farms located in Sweden. Our results show that preventive maintenance planning gives some effect, if the wind turbine components in question live significantly shorter than the turbine itself