Design and Application of Neural Network for Compensation of VSI Output Voltage Nonlinearities

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F24%3APU152437" target="_blank" >RIV/00216305:26620/24:PU152437 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10905982/authors#authors" target="_blank" >https://ieeexplore.ieee.org/document/10905982/authors#authors</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IECON55916.2024.10905982" target="_blank" >10.1109/IECON55916.2024.10905982</a>

Result language

angličtina

Original language name

Design and Application of Neural Network for Compensation of VSI Output Voltage Nonlinearities

Original language description

Voltage source inverters (VSI) with modern power-switching elements are often used to control industrial AC motors. However, the non-linearities of the inverters, such as dead time, turn-on and turn-off switching delay times and voltage drops, are often behind the distortion of the phase currents of the controlled motor. The current distortions can be suppressed by appropriately calculated non-linear functions, which represent the compensation voltages and are consequently added to the control values of the current regulators in the field-oriented control (FOC) algorithm. An artificial neural network (ANN) was designed to identify the non-linear functions of the compensation voltages, which is presented in this paper. Only signals available in the FOC algorithm are used as ANN inputs. The learning process of the neural network takes place online during the running of the motor control algorithm. The learning pattern is generated in each step of the control algorithm from the control errors of the current controllers and the previous ANN outputs. It is not necessary to know the VSI parameters when learning the neural network. The proposed ANN and back-propagation learning algorithm were implemented on one core of the AURIX microcontroller TC397. The proposed strategy was validated through experiments on a real permanent magnet synchronous motor (PMSM), and experimental results prove the effectiveness of the ANN.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IECON 2024- 50th Annual Conference of the IEEE Industrial Electronics Society

ISBN

978-1-6654-6454-3

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

1-6

Publisher name

IEEE

Place of publication

Chicago, IL, USA

Event location

Chicago

Event date

Nov 3, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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