Experience of Using a Neural Network for Magnetic Cores Testing


DEREBAŞI N.

JOURNAL OF SUPERCONDUCTIVITY AND NOVEL MAGNETISM, vol.34, no.5, pp.1409-1414, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1007/s10948-021-05846-6
  • Journal Name: JOURNAL OF SUPERCONDUCTIVITY AND NOVEL MAGNETISM
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Chemical Abstracts Core, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1409-1414
  • Keywords: Artificial neural networks, Nanocrystalline magnetic cores, Power loss
  • Bursa Uludag University Affiliated: Yes

Abstract

Power loss in varied nanocrystalline toroidal cores was measured by using a modified and fully automated measuring system at induction frequency from 1 to 100 kHz and peak magnetic flux density from 0.1 to 1.0 T. Artificial neural network has been successfully used to analyse these collected data and predicted the power loss. In the developed model, the input parameters were outer and inner diameters, strip width, frequency and flux density, while the output parameter was the power loss. When the developed model was tested by untrained sample data, the average correlation of the model was found to be 99% and the overall prediction error was 0.23%. All models are developed with ANN and the results are in good agreement with the experimental results and they were within the acceptable limits.