In-situ digital twinning of induction machines via sensitivity-based genetic algorithm parameter estimation

This paper presents a novel, non-invasive methodology for creating a high-fidelity digital twin using only steady-state operational data. The proposed approach employs a fourstep workflow with two-stage parameter identification algorithm. First, a grid-based sensitivity analysis is conducted to establish robust and constrained search boundaries. Subsequently, a genetic algorithm performs a precise search within these boundaries to identify the final T-equivalent circuit parameters. The methodology was validated on a 1.5 kW induction motor. All but core loss identified parameters demonstrated similarity with those obtained from standard offline tests, and the resulting digital twin accurately reproduced the machine?s behaviour when compared to experimental measurements.