Year: 2024
Type: conference-paper
Abstract: This paper extends the previously published parameter estimation-based approach to interturn short circuit diagnostics in permanent magnet synchronous motors by real-time monitoring of hidden machine states after fault occurrence. The designed monitoring method relies on an adaptive formulation of the Kalman filter, which assumes interdependence between measurement and process noise variables. A variable forgetting factor not only mitigates the impact of the process model uncertainty but also facilitates the simultaneous operation of the monitoring algorithm and fault indicator estimation. Furthermore, contributions of fault current and healthy machine model to stationary reference frame currents are estimated from an advanced discrete-time motor description reflecting a stator winding arrangement inside a motor’s case. The monitoring algorithm is validated in steady state, torque load transient, and velocity transient laboratory experiments with diverse fault severity values.
Authors: Lukas Zezula, Petr Blaha
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G12500569, Analysis of Discrete and Continuous Dynamical Systems with Emphasis on Identification Problems, G3622461675, G3948935741, G4131035492, Artificial Intelligence using Quantum measured Information for realtime distributed systems at the edge, Research and Innovation Centre on Advanced Industrial Production
OpenAlex ID: W4408281342