Publication detail

Online neural network application for compensation of the VSI voltage nonlinearities

BUCHTA, L. KOZOVSKÝ, M.

Original Title

Online neural network application for compensation of the VSI voltage nonlinearities

Type

conference paper

Language

English

Original 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.

Keywords

dead-time compensation, artificial neural network (ANN), voltage source inverter (VSI), permanent magnet synchronous motor (PMSM)

Authors

BUCHTA, L.; KOZOVSKÝ, M.;

Released

16. 10. 2023

Publisher

IEEE

Location

Singapur

ISBN

979-8-3503-3182-0

Book

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

Pages from

1

Pages to

6

Pages count

6

URL

https://ieeexplore.ieee.org/document/10312305

Full text in the Digital Library

http://hdl.handle.net/11012/245227

BibTex

@inproceedings{BUT185462,
  author="Luděk {Buchta} and Matúš {Kozovský}",
  title="Online neural network application for compensation of the VSI voltage nonlinearities",
  booktitle="IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society",
  year="2023",
  pages="6",
  publisher="IEEE",
  address="Singapur",
  doi="10.1109/IECON51785.2023.10312305",
  isbn="979-8-3503-3182-0",
  url="https://ieeexplore.ieee.org/document/10312305"
}