Induction motor drive with field-oriented control and speed estimation using feedforward neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10248771" target="_blank" >RIV/61989100:27240/20:10248771 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9269215" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9269215</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EPE51172.2020.9269215" target="_blank" >10.1109/EPE51172.2020.9269215</a>
Alternative languages
Result language
angličtina
Original language name
Induction motor drive with field-oriented control and speed estimation using feedforward neural network
Original language description
The paper presents the results of our research on the use of artificial neural networks for sensorless control of induction motor drives. A feedforward artificial neural network with one hidden layer was designed and trained offline to act as a model of induction motor, which directly provides the actual speed of a drive. The model was subsequently incorporated in the field-oriented control scheme, where it fully replaces an incremental encoder. The presented solution was tested out using an experimental drive equipped with a 2.2 kW induction machine and controlled by a control system which is based on the TMS320F28335 digital signal controller. The obtained experimental results show a high level of accuracy in the low speed range. (C) 2020 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 21st International Scientific Conference on Electric Power Engineering (EPE) : conference proceedings : 19-21 October 2020, Prague, Czech Republic
ISBN
978-1-72819-480-6
ISSN
2376-5623
e-ISSN
2376-5631
Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Praha
Event date
Oct 19, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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