NDLI: The estimation of iron losses in a non-oriented electrical steel sheet based on the artificial neural network and the genetic algorithm approaches

Content Provider	IEEE Xplore Digital Library
Author	Reljic, D.D. Matic, D.Z. Jerkan, D.G. Oros, D.V. Vasic, V.V.
Copyright Year	2014
Description	Author affiliation: Fac. of Tech. Sci., Univ. of Novi Sad, Novi Sad, Serbia (Reljic, D.D.; Matic, D.Z.; Jerkan, D.G.; Oros, D.V.; Vasic, V.V.)
Abstract	Cold rolled non-oriented (CRNO) electrical steel sheets are soft ferromagnetic materials which are commonly used for electromagnetic core design for AC rotating electrical machines. When these materials are exposed to time-varying magnetic fields, the iron losses occur. These losses represent the power dissipated in the ferromagnetic material and they are dependent upon the frequency and magnetic flux density level of the applied time-varying magnetic field. In order to achieve high-efficiency electrical machines, especially at high operating frequencies and magnetic flux density levels, iron losses should be kept as low as possible. This imposes the need for more accurate iron losses models, but also for fast and reliable estimation techniques. This paper considers the applications of an artificial neural network (ANN) and a genetic algorithm (GA), based on the classical iron losses separation formulation for a fast estimation of the specific iron losses in CRNO electrical steel sheet grade M530-50A over a wide frequency and magnetic flux density range. Iron losses measurement data, provided by the manufacturer, are used to calibrate the iron losses models. The approaches were verified using the manufacturer's measurement data. Acceptable accuracy was obtained.
Sponsorship	IEEE Croatia Sect.
Starting Page	51
Ending Page	57
File Size	286323
Page Count	7
File Format	PDF
ISBN	9781479924493
DOI	10.1109/ENERGYCON.2014.6850405
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-13
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic flux density Estimation Artificial neural networks Iron Loss measurement Iron losses estimation Mathematical model Equations Genetic algorithms
Content Type	Text
Resource Type	Article

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