Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8122
Title: Modeling magnetic hysteresis under DC-biased magnetization using the neural network
Authors: Zhao, Z
Liu, F
Ho, SL 
Fu, WN 
Yan, W
Keywords: DC-biased excitation
Hysteresis model
Magnetic property
Neural-network theory
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on magnetics, 2009, v. 45, no. 10, 5257257, p. 3958-3961 How to cite?
Journal: IEEE transactions on magnetics 
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 .
URI: http://hdl.handle.net/10397/8122
ISSN: 0018-9464
EISSN: 1941-0069
DOI: 10.1109/TMAG.2009.2023070
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