Symmetrical spherical roller bearings (SSRB) used as main bearings for wind turbines are known for their high load carrying capacity. Nevertheless, even designed after state-of-the-art guidelines premature failures of this bearing type occur. One promising solution to overcome this problem are asymmetrical spherical roller bearings (ASRB). Using ASRB the contact angles of the two bearing rows can be adjusted individually to the load situation occurring during operation. In this study the differences between symmetrical and asymmetrical spherical roller bearings are analyzed using the finite element method (FEM). Therefore, FEM models for a three point suspension system of a wind turbine including both bearings types are developed. These FEM models are validated with measurement data gained at a full-size wind turbine system test bench. Taking into account the design loads of the investigated wind turbine it is shown that the use of an ASRB leads to a more uniform load distribution on the individual bearing rows. Considering fatigue-induced damage an increase of the bearing life by 62% can be achieved. Regarding interactions with other components of the rotor suspension system it can be stated that the transfer of axial forces into the gearbox is decreased significantly.

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Due to a small cross-sectional dimension compared to their diameter, pitch bearings have a comparably low stiffness. Therefore, surrounding structures strongly influence the internal load distribution of the bearing. By the use of stiffening plates, the load situation of pitch bearings can be positively affected. With a gradient-based optimization procedure regarding contact force and contact angle distribution, an optimized plate shape is determined. This optimized shape provides at the same time a more equalized load distribution, while minimizing the effect of contact ellipse truncation.

Wind energy is one of the most important technologies for a climate-neutral energy supply. However, the premature failure of wind turbines due to unknown loads leads to a reduction in competitiveness compared to other energy sources. Here, load monitoring systems can make a significant contribution to the prevention of such failures. Most load monitoring systems for wind turbines focus on strain signals of structural components as the tower, main shaft or the rotor blade root. Based on these signals, axial forces, torsion or bending moments, which are acting on these components, are calculated. But this provides only partial information about the complete load situation, as transverse forces are not considered. Other methods use simulation or measurement data to train artificial neural networks (ANN) to estimate damage-equivalent loads acting on a wind turbine. However, this approach is accompanied by a loss of information, because the individual load components are condensed to an equivalent. In this work a method is presented that enables a measurement of rotor and main bearing loads considering all their individual load components. For this purpose, an ANN is trained with elastic multibody simulation (eMBS) data. Based on displacement signals, acting rotor and main bearing loads are estimated. The results show that even with consideration of nonlinearities, including nonlinear stiffness curves and bearing clearances, an appropriate accuracy can be achieved using the method presented.

Abstract. The premature failure of wind turbines due to unknown loads leads to a reduction in competitiveness compared to other energy sources. Especially the failure of main bearings results in high costs and downtimes, as for an exchange of this component the rotor needs to be demounted. Load monitoring systems can make a significant contribution to understand and prevent such failures. However, most load monitoring systems do not take into account the main bearing loads in particular as there is no commercially applicable measuring system for this purpose. This work shows how main bearing loads can be estimated using virtual sensors. For this purpose, several regression models are trained with test bench data considering strain and displacement signals. It is investigated with which combination of signal type and regression model the highest accuracy is achieved. The results show that for either using strain or displacement signals an appropriate accuracy can be achieved. In particular, it is shown that a linear regression with interactions already achieves good accuracy and that further increases in regression model complexity do not add significant value.