Data-Driven Model Identification of Unbalanced Induction Motor Dynamics and Forces using SINDYc

TL;DR

This paper employs the SINDYc method to accurately identify the dynamics and forces of an unbalanced induction motor from time-series data, improving torque estimation and enabling real-time control applications.

Contribution

It introduces a data-driven SINDYc modeling approach for unbalanced induction motors, achieving high accuracy in torque and magnetic pull estimation from measurable signals.

Findings

Achieved 8.8 mNm mean absolute error in torque estimation.

Reduced torque estimation error by 65% compared to reference equations.

Model is computationally efficient for control integration.

Abstract

This paper identifies the stator currents, torque and unbalanced magnetic pull (UMP) of an unbalanced induction motor by the System Identification of Nonlinear Dynamics with Control (SINDYc) method from time-series data of measurable quantities. The SINDYc model has been trained on data coming from a nonlinear magnetic equivalent circuit model for three rotor eccentricity configurations. When evaluating the SINDYc model for static eccentricity, torques and UMPs with excellent accuracies, i.e., 8.8 mNm and 4.87 N of mean absolute error, respectively, are found. When compared with a reference torque equation, this amounts to a 65% error reduction. For dynamic eccentricity, the estimation is more difficult. The SINDYc model is fast enough to be embedded in a control procedure.