为了提高用于建筑电梯系统的圆筒型永磁直线同步电机(Tubular Permanent Magnet Linear Synchronous Machine,TPMLSM)建模计算效率、改进电机的优化设计方法,提出了一种基于子域解析法和自适应遗传算法的TPMLSM建模与优化方法,首先根据麦克斯韦方程组算得电机的磁通密度,使用气隙磁通密度积分法和麦克斯韦张量法分别算得空载反电势和定位力.基于解析模型,使用自适应遗传算法优化TPMLSM的初级铁心长度、轭部高度、齿槽宽度等尺寸参数,最后加工制作了优化后的样机进行实验测试,并建立了相应的有限元模型对比验证.实验结果表明,优化后的TPMLSM定位力减小了 43.4％,电机解析模型与有限元模型的定位力计算误差为1.37％,解析计算速度是有限元法的4.7倍.
Optimization Design of Tubular Permanent Magnet Linear Machine for Building Elevator
In order to improve the modeling efficiency of tubular permanent magnet linear synchronous machine(TPMLSM)used in building elevator system,improve machine optimization method,a TPMLSM modeling and optimization method based on sub-domain analytical method and adaptive genetic algorithm was proposed.Firstly,the magnetic flux density of the machine was calculated according to Maxwell equations.The air gap flux density integral method and Maxwell tensor method were used to cal-culate the no-load back EMF and detent force respectively.Based on the analytical model,the adaptive genetic algorithm was used to optimize the primary core length,yoke height,slot width and other size parameters of the TPMLSM.Finally,the opti-mized prototype was manufactured for experimental testing,and the corresponding finite element model was established for verifi-cation.The results show that the detent force of optimized TPMLSM is reduced by 43.4％,the calculation error of detent force be-tween the analytical model and finite element model is 1.37％,and the analytical computing speed is 4.7 times that of finite ele-ment method.
Building ElevatorTubular Permanent Magnet Linear Synchronous MachineDetent ForceSubdo-main Analytical MethodAdaptive Genetic AlgorithmThrust Optimization
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