No abstract

The energy efficiency of a power plant is largely determined by the vibrations of bearings that hold the shaft rotating at high speed which need to be critically controlled. This study presents the relative vibration modeling of a shaft bearing that is installed in a 660 MW supercritical steam turbine system. The operational data in raw form after being cleaned using machine learning based visualization and extensive data processing helped in training and validation of SVM and ANN models which are then compared by external validation tests. The model with best results is then used for the simulations of constructed operating scenarios. The ANN has been further tested for the complete operational load range (353 MW to 662 MW) which predicted the reduction in relative vibrations. Moreover, the validated ANN model has been used to develop many strategies of vibration reduction which helped in achieving more than 4% reduction in relative vibrations. Subsequently, an operational strategy that predicts a significant reduction in the bearing vibration levels is selected. For confirmation of the accuracy of prediction by ANN process model, the selected strategy has been used with the actual power plant. This assures the significant reduction of bearing vibration less than the alarm limit.