Identification of Thermal Model Parameters Using Deep Learning Techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43965479" target="_blank" >RIV/49777513:23220/22:43965479 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9831641" target="_blank" >https://ieeexplore.ieee.org/document/9831641</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISIE51582.2022.9831641" target="_blank" >10.1109/ISIE51582.2022.9831641</a>
Alternative languages
Result language
angličtina
Original language name
Identification of Thermal Model Parameters Using Deep Learning Techniques
Original language description
Identification of thermal model parameters using multi-step prediction is proposed. Even in the case of a linear model, the multi-step prediction is a non-linear complex function, hence we use techniques of deep learning for its identification. Specifically, we use stochastic gradient descent optimization with importance sampling of mini-batches. The importance function is designed to match the character of thermal experiments in which the step change is less frequent than steady-state operation. The proposed method is demonstrated on the identification of an IGBT module SK 20 DGDL 065 ET. The maximum error of the model identified by the multi-step approach is almost two times smaller than that of the model identified by the least squares.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Electrical Engineering Technologies with High-Level of Embedded Intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) : /proceedings/
ISBN
978-1-66548-240-0
ISSN
2163-5145
e-ISSN
—
Number of pages
4
Pages from-to
978-981
Publisher name
IEEE
Place of publication
Piscataway
Event location
Anchorage, Alaska, USA
Event date
Jun 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000946662000151