Evaluation and Discussion of Virtual Sensor for Torque Prediction in Induction Machines under Dynamic Conditions

This study explores the utilization of a machine learning approach for the prediction of torque in an induction machine. It focuses on the assessment of a model that has demonstrated efficacy in stationary torque prediction, which is evaluated in this study under dynamic conditions. Gaussian process regression is used for this purpose. A key feature of this approach is that the machine learning model is trained with stationary operating points. The experimental results demonstrate that machine learning models encounter specific challenges under dynamic load conditions, particularly due to systematic errors that compromise prediction accuracy. The experimental validation was conducted using a back-to-back test bench configuration with two identical 440kW induction machines. Additionally, a power analyzer was installed at the test setup to record time-synchronous electrical measured variables. The findings offer significant insights into the optimization of a machine learning approach for dynamic operating conditions and underscore the challenges associated with their practical implementation in real drive systems.