DIGITAL TWINS FOR LARGE ELECTRIC DRIVE TRAINS

Abstract

The potential of data driven operational support with respect to predictive analysis is limited. A new approach is the model based simulation of operational behavior. The simulation of specific physical effects allows monitoring of the system behavior even of data that cannot be measured directly. A simulation model that supports the plant monitoring is called digital twin. It provides additional information about the asset state. Better knowledge of the system behavior increases the availability of the plant and the possibility to predict potential faults during operation.This paper presents two examples of digital twins. The first one, which is realized for a 50MW electric drive train, is designed to identify the actual unbalance state of the rotor system. The second one is designed to optimize the run up routines for synchronous motors with DOL start. It calculates the current rotor temperature based on the transferred losses and predicts the temperature for switching-on scenarios.The mathematical methods to implement digital twins are explained in detail. The results of numerical simulations are compared to measurements on the real system. Finally, the benefits of the digital twin in terms of failure diagnosis and system state predictions are presented.