State-Dependent Neural 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%2F25%3A43975553" target="_blank" >RIV/49777513:23220/25:43975553 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964320" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964320</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TII.2025.3552719" target="_blank" >10.1109/TII.2025.3552719</a>
Alternative languages
Result language
angličtina
Original language name
State-Dependent Neural Flux Linkage Models of Synchronous Machines
Original language description
Flux linkage maps (FLMs) are routinely used in high-precision control and modeling of synchronous machines. Common methods often consider only the dependence of the FLM on the stator currents, allowing for convenient representation in lookup tables or neural networks. However, the flux linkage also depends on speed, position, and other state variables. Although this is formally simple to add as an additional input to neural models of FLM, the estimation with additional inputs becomes more demanding. We demonstrate that the conventional approach of FLM training using the assumption of a steady-state regime is insufficient to learn the dependency on the rotor position. It is necessary to use the complete ordinary differential equation of the current to learn the FLM model. Even for a shallow neural model of the FLM, the estimation procedure yields a deep learning task known as neural ODE. This procedure essentially generates multistep ahead prediction of differential equations and minimizes the mismatch between the mathematical model and data. The efficiency of this approach is demonstrated on the FLM of a synchronous machine considering flux saturation, speed dependence, and slot harmonics. The proposed approach significantly improves current prediction, yielding improved deadbeat current control. The results are experimentally verified on a 4.5 kW laboratory prototype.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/EH23_021%2F0008999" target="_blank" >EH23_021/0008999: New technologies for clean mobility</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Industrial Informatics
ISSN
1551-3203
e-ISSN
1941-0050
Volume of the periodical
21
Issue of the periodical within the volume
7
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
5331-5339
UT code for WoS article
001480212300001
EID of the result in the Scopus database
2-s2.0-105003037062