Title: Identification of Thermal Model Parameters Using Deep Learning Techniques
Authors: Ševčík, Jakub
Šmídl, Václav
Votava, Martin
Citation: ŠEVČÍK, J. ŠMÍDL, V. VOTAVA, M. Identification of Thermal Model Parameters Using Deep Learning Techniques. In 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) : /proceedings/. Piscataway: IEEE, 2022. s. 978-981. ISBN: 978-1-66548-240-0 , ISSN: 2163-5145
Issue Date: 2022
Publisher: IEEE
Document type: konferenční příspěvek
URI: 2-s2.0-85135786103
ISBN: 978-1-66548-240-0
ISSN: 2163-5145
Keywords in different language: deep learning;junction temperature;multistep prediction;neural network;ordinary least squares;thermal model
Abstract in different language: 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.
Rights: Plný text je přístupný v rámci univerzity přihlášeným uživatelům.
Appears in Collections:Konferenční příspěvky / Conference papers (RICE)
Konferenční příspěvky / Conference Papers (KEV)

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