Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines

نویسندگان

چکیده

The development of condition monitoring (CM) systems induction machines (IMs) is essential for the industry because early fault detection would help engineers to optimise maintenance plans. However, use several IMs test and validate diagnosis methods developed requires also costly benches that, anyway, often face limitations in range faults operating conditions be tested. To avoid it, accurate models such as those based on finite element method (FEM) reduce major drawbacks but their inability execute FEM real time largely reduces application on-line continuous systems. alleviate this problem a hybrid FEM-analytical model has been proposed. It uses an analytical that can run real-time hardware loop (HIL) system, after its parameters have computed through simulations. In way, proposed provides high accuracy at cost long simulation times computational costs (both computing power memory resources) compute IM parameters. This work aims reducing these drawbacks. particular, sparse identification techniques balances complexity by selecting number simulations accurately coupling with different severity degrees. Particularly, methodology applied develop abnormal eccentricity levels related mechanical produce most breakdowns.

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ژورنال

عنوان ژورنال: International Journal of Electrical Power & Energy Systems

سال: 2021

ISSN: ['1879-3517', '0142-0615']

DOI: https://doi.org/10.1016/j.ijepes.2020.106417