State-Dependent Neural Flux Linkage Models of Synchronous Machines

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

State-Dependent Neural Flux Linkage Models of Synchronous Machines

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

State-Dependent Neural Flux Linkage Models of Synchronous Machines

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EH23_021%2F0008999" target="_blank" >EH23_021/0008999: Nové technologie pro čistou mobilitu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Industrial Informatics

ISSN

1551-3203

e-ISSN

1941-0050

Svazek periodika

21

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

5331-5339

Kód UT WoS článku

001480212300001

EID výsledku v databázi Scopus

2-s2.0-105003037062