Method for optimizing the magnetic circuit of a linear generator using FEM simulations

被引:7
|
作者
Sjolund, J. [1 ]
Leijon, M. [1 ,2 ]
Eriksson, S. [1 ]
机构
[1] Uppsala Univ, Div Elect, S-75121 Uppsala, Sweden
[2] Chalmers Univ Technol, Div Elect Machines & Power Elect, S-41296 Gothenburg, Sweden
关键词
SYNCHRONOUS GENERATOR; DEMAGNETIZATION RISK; PERMANENT-MAGNETS; OPTIMIZATION;
D O I
10.1063/1.5129303
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Within the area of permanent magnet electrical machines, there is an ongoing focus on replacing the rare earth permanent magnets with alternatives. An option is hard ferrites, commonly used in other applications. The relatively low coercive field strength of the ferrite magnets makes irreversible demagnetization an area that should not be neglected. In this paper, a methodology is proposed for the optimization of a slow-moving linear generator simulated in a finite element environment. The no-load phase voltage is maximized while accounting for iron saturation and permanent magnet irreversible demagnetization. This demagnetization is considered when the translator is alongside either the stator or air. The inclination angle between magnetization and magnetic field strength is accounted for by adjusting the intrinsic coercivity for each element of the permanent magnets. Characteristics for the magnet grades Y30 and Y40 are used in the optimization process. The velocity of the translator is set to resemble a speed common to wave power applications. Commercial finite element software is used together with two optimization algorithms: the genetic algorithm and the particle swarm optimization. The results of these optimization algorithms reach similar optimal solutions for the considered objective function, assuring a result close to a global maximum. The results also show a great difference in the optimal geometry for the two magnet grades and highlight the need to account for irreversible demagnetization when designing generators with ferrite magnets. (C) 2020 Author(s).
引用
收藏
页数:12
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