NDLI: Optimization of magnets segmentation for eddy current losses reduction in permanent magnets electrical machines

Content Provider	IEEE Xplore Digital Library
Author	Belli, Z. Mekideche, M.R.
Copyright Year	2013
Description	Author affiliation: Electr. Eng. Dept., Jijel Univ., Jijel, Algeria (Belli, Z.; Mekideche, M.R.)
Abstract	Magnet segmentation is an effective and simple technique for eddy current losses reduction in high power permanent magnets electrical machines. This work aims to study the magnets segmentation effects on eddy current losses in magnets. First, an analysis of eddy currents and associated losses in magnets is made. After, the magnets segmentation effects on losses reduction in the range of the industrial frequencies from few Hz to dozen of kHz are investigated. To achieve these tasks, a 2D non linear model based on finite element analysis is developed under MATLAB environment. The developed model is applied to a synchronous machine with surface mounted permanent magnets. In a second part of the work, we have adopted an optimization process which consists to associate the finite element analysis to genetic algorithm. In this stage of the work, we aim to find the best parameters of magnets segmentation and avoid any segmentations anomaly that appears under some conditions such as skin effect, and which can leads to losses increases instead of their reduction.
Sponsorship	IEEE Ind. Appl. Soc.
Starting Page	1
Ending Page	7
File Size	1720747
Page Count	7
File Format	PDF
ISBN	9781467352697
e-ISBN	9781467352710
DOI	10.1109/EVER.2013.6521595
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-27
Publisher Place	Monaco
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximations Windings Finite elements analysis Conductivity Frequency conversion Permanent magnets Equations Eddy current losses Genetic algorithm Skin Nickel Losses reduction Magnets segmentation
Content Type	Text
Resource Type	Article

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