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The use of journal bearings instead of rolling bearings as planetary bearings in wind turbine (WT) gearboxes is advantageous for the power density of the drive train. In addition, they can increase the reliability of the gearboxes as they can be operated wear-free in hydrodynamic operation, i.e. with fluid friction. The dynamic loading conditions in wind turbines, as well as special conditions such as insufficient lubrication and particle contamination, can lead to mixed friction operation and consequently to wear in the journal bearings. If mixed friction is not detected, damage and failure of the journal bearing and consequently of the WT gearbox may occur. Therefore, it is required to develop a Condition Monitoring System (CMS) to detect mixed friction in the journal bearings during operation of the WT. Preliminary investigations have shown that various friction conditions can be detected by acoustic measurements in combination with machine learning classifiers. Current investigations on CMS methods for journal bearings using acoustic measurement methods are limited to component level applications. A condition monitoring methodology for wind turbine gearbox journal bearings does not currently exist. A major challenge for CMS development for journal bearings in WT gearboxes is the transfer of methods already proven at component level to gearbox applications. In preparation for CMS application at the gearbox level, this paper presents an approach for monitoring different friction conditions of journal bearings based on acoustic measurements at a component test rig. For the classification of the friction state, different machine learning (ML)-based approaches trained on the acquired acoustic measurement data are compared with respect to the achieved classification accuracy. Knowledge of the robustness of the classification method, e.g. with respect to the distance of the sensor to the bearing, provides the necessary basis for the use of the CMS at the gearbox level. The investigations are carried out under operating conditions typical for planetary bearings in wind turbines. Classification performance is evaluated using a validated elasto-hydrodynamic simulation model. The aim of the work is to develop a method that detects friction classes in the journal bearing based on structure-borne sound measurements. Here, simulation results are used to train the algorithms. Finally, the demonstrated method will be successfully applied to a test rig for wind turbine gearbox journal bearings. Bas- ed on the results, an ML approach will be selected for application in gearboxes. [ABSTRACT FROM AUTHOR]

Summary: Fluctuating residual loads force lignite‐ and hard‐coal‐fired power plants to operate at technical and economical limitations. Energy storage systems (ESS) could resolve the plant‐design restrictions and enlarge the overall bandwidth of operation. Therefore, the electricity during minimum load can be stored and later fed into the grid. For this, the occurrence of minimum loads which could enable the periodic charging of ESS is analyzed at the example of lignite‐ and hard‐coal‐fired power plants in Germany. The durations of the minimum loads show marginal deviations within the considered years, as the median varies between 3 and 4 hours. The median of hard coal is 4 hours and for lignite, between 2 and 3 hours, respectively. In contrast to hard coal, the number of minimum loads of lignite increases slightly from 2016 to 2018. In total, the number and duration of minimum loads of both generation types are aligning within all transmission system areas. This is an indication that lignite power plants are increasingly affected by the merit‐order and are gradually being forced to operate more flexibly. Based on the analysis, a rough estimate of a suitable storage size for the integration of an ESS technology was derived for an exemplary power plant. [ABSTRACT FROM AUTHOR]