Data-Driven Modeling of Inverter-Fed Induction Motor Drives using DMDc for Faulty Conditions

Modeling faulty behavior of systems has benefits in diagnosis and control. In this paper a data-driven method, dynamic mode decomposition with control (DMDc), is employed for modeling an inverter-fed induction machine. Results are shown and compared for two scenarios: A step input change and an inverter fault. For both cases, the algorithm can correctly predict behavior of the system. The advantage of this model is its independence from the system parameters. The results show promise for data-drivenfault diagnostics and system modeling.

Automated summary

This study utilizes a data-driven method, DMDc, to model faulty behavior of an inverter-fed induction machine. Results demonstrate the algorithm's ability to accurately predict system behavior, independent of system parameters. The model shows promise for data-driven fault diagnostics and system modeling.