Modeling magnetic hysteresis under DC-biased magnetization using the neural network

Zhigang Zhao, Fugui Liu, Siu Lau Ho, Weinong Fu, Weili Yan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

The excitation conditions of electrical steel are generally sinusoidal but, with the advent of power electronics in recent years, dc-biased excitation is sometimes experienced. The use of an iron core under dc-biased magnetization gives rise to asymmetrical hysteresis loops and the hysteresis loss in the iron core also increases with the value of dc excitation. For iron cores working with dc-biased excitation, accurate modeling of the nonlinear characteristics for the iron core that includes the dc-bias is very important for the computation of the exciting current and the iron loss. In this paper, an efficient approach for simulating the hysteresis loop of iron core under dc-biased excitation using neural-network theory is presented. The proposed method has the merits that a specific hysteresis loop can be identified conveniently and effectively to ensure that accurate electromagnetic-field analysis can be realized .
Original languageEnglish
Article number5257257
Pages (from-to)3958-3961
Number of pages4
JournalIEEE Transactions on Magnetics
Volume45
Issue number10
DOIs
Publication statusPublished - 1 Oct 2009

Keywords

  • DC-biased excitation
  • Hysteresis model
  • Magnetic property
  • Neural-network theory

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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