Online neural network application for compensation of the VSI voltage nonlinearities
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Date
2023-10-16
Authors
Buchta, Luděk
Kozovský, Matúš
0000-0002-8954-3495Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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Abstract
The paper aims to solve the distortion problem of the inverter output voltages that cause harmonic deformation of the phase currents and ripple of dq- currents of the three-phase permanent magnet synchronous motor (PMSM). The inverter non-linearities adversely affect the effectiveness of the PMSM control algorithm. The compensation strategy is based on the neural network and knowledge of the three-phase PMSM model structure and its parameters. The input data for the neural network consist of the normed values and detected polarities of the phase currents and rotor position information. As a result, the proposed artificial neural network (ANN) can extract non-linear functions from the measured data to compensate for the VSI output voltages. The ANN is designed to learn online while the PMSM is running. The back-propagation algorithm is used for neural network learning. The proposed stratégy was implemented in an AURIX TC397 microcontroller and validated by experiments on a real PMSM. The presented results demonstrate the effectiveness of the proposed solution.
Description
Citation
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. 2023, p. 1-6.
https://ieeexplore.ieee.org/document/10312305
https://ieeexplore.ieee.org/document/10312305
Document type
Peer-reviewed
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Accepted version
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Language of document
en
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Date of acceptance
Defence
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(C) IEEE