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Multifrequency nonlinear model of magnetic material with artificial intelligence optimization.

J Pawłowski1, K Kutorasiński2, M Szewczyk3

  • 1Department of Theoretical Physics, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wyb. Wyspiańskiego 27 St., 50-370, Wrocław, Poland.

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|November 17, 2022
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Summary
This summary is machine-generated.

This study introduces an advanced AI-optimized magnetic ring model for power systems. The new model accurately captures frequency and current dependencies, improving stability in transient simulations.

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Computational Physics

Background:

  • Magnetic rings are crucial components in high-frequency, high-current power applications like switchgear.
  • Existing models struggle with numerical instabilities due to uneven parameter characteristics under varying current conditions.

Purpose of the Study:

  • To develop a general, AI-optimized model for magnetic rings that accurately reflects frequency and current dependencies.
  • To improve the stability and accuracy of power system transient simulations.

Main Methods:

  • Utilized artificial intelligence (AI) metaheuristic optimization methods.
  • Developed a lumped element equivalent circuit model.
  • Incorporated Langevin function for smooth parameter characteristics.
  • Extended the Jiles-Atherton (JA) model to include frequency-dependent magnetic saturation.

Main Results:

  • Achieved a perfect fit for the magnetic ring model across frequencies up to 100 MHz and currents up to saturation.
  • Ensured smooth parameter characteristics, resolving numerical instabilities in transient simulations.
  • Demonstrated the feasibility of a frequency-dependent Jiles-Atherton model.

Conclusions:

  • The AI-optimized magnetic ring model offers superior accuracy and stability for power system transient analysis.
  • This work enables more reliable simulations in demanding electrical power applications.
  • The development of a frequency-dependent JA model advances magnetic material modeling.