Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks

  • Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at constant speed and current reference values. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation. The simulation model is based on flux and torque maps, depending on dq-currents and the electrical angle. The maps are generated from FEM simulation of an interior permanent magnet synchronous machine (IPM) and are published with the paper. They are intended to serve other researchers for direct comparison with their own methods. Simulation results in this paper verify that by using ILC and NNs together, current harmonics in transient operation can be eliminated better than without NNs.

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Metadaten
Author:Annette Mai, Xinjun Liu, Bernhard Wagner, Maximilian Hofmann
DOI:https://doi.org/10.3390/machines11080784
ISSN:2075-1702
Parent Title (English):Machines
Publisher:MDPI AG
Document Type:Article
Language:English
Date of first Publication:2023/07/28
Release Date:2024/09/10
Volume:11
Issue:8
Article Number:784
institutes:Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik
Research Themes:Energie & Ressourcen
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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