Neural ODE for Estimation of Flux Linkage Models of Synchronous Machines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F23%3A43969715" target="_blank" >RIV/49777513:23220/23:43969715 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10312498" target="_blank" >https://ieeexplore.ieee.org/document/10312498</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IECON51785.2023.10312498" target="_blank" >10.1109/IECON51785.2023.10312498</a>
Alternative languages
Result language
angličtina
Original language name
Neural ODE for Estimation of Flux Linkage Models of Synchronous Machines
Original language description
An accurate estimation of flux linkage maps is essential for the proper control and modeling of synchronous machines. We propose to use neural networks as the flux linkage model with training procedure respecting the differential equation of the stator current. Moreover, the neural network allows straightforward extension of the number of input variables. We demonstrate this ability to estimate the flux linkage as a functionof rotor speed and position modulated by slot harmonics. The proposed approach is demonstrated on real interior permanent magnet synchronous machine data. The results demonstrate a significant improvement in current prediction compared to commonly used methods.
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
<a href="/en/project/TN02000054" target="_blank" >TN02000054: Božek Vehicle Engineering National Center of Competence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IECON Proceedings (Industrial Electronics Conference)
ISBN
979-8-3503-3182-0
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Piscaway
Event location
Singapore
Event date
Oct 16, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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