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Optimization-Based Network Identification for Thermal Transient Measurements

Author

Listed:
  • Nils J. Ziegeler

    (Faculty of Electrical Engineering, South Westphalia University of Applied Sciences, 59494 Soest, Germany)

  • Peter W. Nolte

    (Fraunhofer Application Center for Inorganic Phosphors, Branch Lab of Fraunhofer Institute for Microstructure of Materials IMWS, 59494 Soest, Germany)

  • Stefan Schweizer

    (Faculty of Electrical Engineering, South Westphalia University of Applied Sciences, 59494 Soest, Germany
    Fraunhofer Application Center for Inorganic Phosphors, Branch Lab of Fraunhofer Institute for Microstructure of Materials IMWS, 59494 Soest, Germany)

Abstract

Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the results of this method are significantly affected by noise in the measured data, which is unavoidable to a certain extent. In this paper, a post-processing procedure for network identification from thermal transient measurements is presented. This so-called optimization-based network identification provides a much more accurate and robust result compared to approaches using Fourier or Bayesian deconvolution in combination with Foster-to-Cauer transformation. The thermal structure function obtained from network identification by deconvolution is improved by repeatedly solving the inverse problem in a multi-dimensional optimization process. The result is a non-diverging thermal structure function, which agrees well with the measured thermal impedance. In addition, the associated time constant spectrum can be calculated very accurately. This work shows the potential of inverse optimization approaches for network identification.

Suggested Citation

  • Nils J. Ziegeler & Peter W. Nolte & Stefan Schweizer, 2021. "Optimization-Based Network Identification for Thermal Transient Measurements," Energies, MDPI, vol. 14(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7648-:d:680016
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    References listed on IDEAS

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    1. Nils J. Ziegeler & Peter W. Nolte & Stefan Schweizer, 2021. "Quantitative Performance Comparison of Thermal Structure Function Computations," Energies, MDPI, vol. 14(21), pages 1-16, October.
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    Cited by:

    1. Krzysztof Górecki & Krzysztof Posobkiewicz, 2022. "Cooling Systems of Power Semiconductor Devices—A Review," Energies, MDPI, vol. 15(13), pages 1-29, June.

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