Diagnostics of Interturn Short Circuits in PMSMs With Online Fault Indicators Estimation

Diagnostics of Interturn Short Circuits in PMSMs With Online Fault Indicators Estimation

WoS Article

This article presents novel model-based diagnostics of interturn short circuits in permanent magnet synchronous machines that enable estimating fault location and its severity, even during transients. The proposed method utilizes recursive parametric estimation and model comparison approaches cast in a decision-making framework to track motor parameters and fault indicators from a machine's discrete-time model. The discrete-time prototype is derived from an advanced motor model that reflects the stator winding arrangement in a motor's case. The fault detection is then performed by tracking the changes in the estimated probability density function of the electrical parameters, using the Kullback-Leibler divergence. The fault location is subsequently evaluated by performing a recursive comparison of the predefined fault models in the different phases, utilizing a growing-window approach. Ultimately, a parametric estimation algorithm applied to the fault current model allows identifying the fault severity. The diagnostic algorithm has been validated via laboratory experiments, and its capabilities are compared with other approaches enabling severity estimation.

This article presents novel model-based diagnostics of interturn short circuits in permanent magnet synchronous machines that enable estimating fault location and its severity, even during transients. The proposed method utilizes recursive parametric estimation and model comparison approaches cast in a decision-making framework to track motor parameters and fault indicators from a machine's discrete-time model. The discrete-time prototype is derived from an advanced motor model that reflects the stator winding arrangement in a motor's case. The fault detection is then performed by tracking the changes in the estimated probability density function of the electrical parameters, using the Kullback-Leibler divergence. The fault location is subsequently evaluated by performing a recursive comparison of the predefined fault models in the different phases, utilizing a growing-window approach. Ultimately, a parametric estimation algorithm applied to the fault current model allows identifying the fault severity. The diagnostic algorithm has been validated via laboratory experiments, and its capabilities are compared with other approaches enabling severity estimation.

Discrete-time systems; fault detection; fault diagnosis; fault location; parameter estimation; permanent magnet motors; recursive estimation

Discrete-time systems; fault detection; fault diagnosis; fault location; parameter estimation; permanent magnet motors; recursive estimation

ZEZULA, L.; KOZOVSKÝ, M.; BLAHA, P.

2025

27.02.2024

IEEE

0278-0046

IEEE Transactions on Industrial Electronics

71

11

United States of America

15001

15011

11

https://ieeexplore.ieee.org/document/10449893

http://hdl.handle.net/11012/245247

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Diagnostics_of_Interturn_Short_Circuits_in_PMSMs_With_Online_Fault_Indicators_Estimation