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Learning Data-Driven Stable Corrections of Dynamical Systems—Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer

Author

Listed:
  • Chady Ghnatios

    (SKF Chair, PIMM Lab, Arts et Metiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France
    All authors contributed equally to this work.)

  • Xavier Kestelyn

    (ULR 2697-L2EP, Centrale Lille, Junia ISEN Lille, Arts et Metiers Institute of Technology, University of Lille, 59000 Lille, France
    All authors contributed equally to this work.)

  • Guillaume Denis

    (RTE R&D, 7C Place du Dôme, 92073 Paris La Defense, CEDEX, France
    All authors contributed equally to this work.)

  • Victor Champaney

    (ESI Chair, PIMM Lab, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France
    All authors contributed equally to this work.)

  • Francisco Chinesta

    (RTE Chair, PIMM Lab, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France
    CNRS@CREATE, 1 Create Way, 04-05 Create Tower, Singapore 138602, Singapore
    All authors contributed equally to this work.)

Abstract

Many engineering systems can be described by using differential models whose solutions, generally obtained after discretization, can exhibit a noticeable deviation with respect to the response of the physical systems that those models are expected to represent. In those circumstances, one possibility consists of enriching the model in order to reproduce the physical system behavior. The present paper considers a dynamical system and proposes enriching the model solution by learning the dynamical model of the gap between the system response and the model-based prediction while ensuring that the time integration of the learned model remains stable. The proposed methodology was applied in the simulation of the top-oil temperature evolution of a power transformer, for which experimental data provided by the RTE, the French electricity transmission system operator, were used to construct the model enrichment with the hybrid rationale, ensuring more accurate predictions.

Suggested Citation

  • Chady Ghnatios & Xavier Kestelyn & Guillaume Denis & Victor Champaney & Francisco Chinesta, 2023. "Learning Data-Driven Stable Corrections of Dynamical Systems—Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer," Energies, MDPI, vol. 16(15), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5790-:d:1210353
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    References listed on IDEAS

    as
    1. Weifeng Chen & Fei Zheng & Shanping Gao & Kai Hu & Saadat Hanif Dar, 2022. "An LSTM with Differential Structure and Its Application in Action Recognition," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-14, May.
    2. Torregrosa, Sergio & Champaney, Victor & Ammar, Amine & Herbert, Vincent & Chinesta, Francisco, 2022. "Surrogate parametric metamodel based on Optimal Transport," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 36-63.
    Full references (including those not matched with items on IDEAS)

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