Optimization of synchronous reluctance motor based on radial basis network

Amir Erfani, J. Faiz
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引用次数: 2

Abstract

This paper presents surrogate-model based optimization for synchronous reluctance motor (SynRm) with transversally laminated rotor. A radial basis function (RBF) model with 12 input variables and three outputs is first trained. A dataset is obtained using finite element method to estimate parameters of RBF model. By building RBF model, the RBF network can predicts the outputs of the SynRm with good accuracy Using non-dominated sorting genetic algorithm (NSGA II), pareto front is obtained. The SynRm is designed to maximize the maximum developed torque and power factor of the motor with constrained torque ripple.
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基于径向基网络的同步磁阻电机优化
提出了一种基于代理模型的横向层合转子同步磁阻电机优化方法。首先训练一个具有12个输入变量和3个输出的径向基函数(RBF)模型。利用有限元法对RBF模型参数进行估计,得到数据集。通过建立RBF模型,RBF网络能够较好地预测SynRm的输出,利用非支配排序遗传算法(NSGA II)得到pareto front。SynRm的设计目的是在约束转矩脉动的情况下,最大限度地提高电机的最大开发扭矩和功率因数。
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来源期刊
Serbian Journal of Electrical Engineering
Serbian Journal of Electrical Engineering Energy-Energy Engineering and Power Technology
CiteScore
1.30
自引率
0.00%
发文量
16
审稿时长
25 weeks
期刊介绍: The main aims of the Journal are to publish peer review papers giving results of the fundamental and applied research in the field of electrical engineering. The Journal covers a wide scope of problems in the following scientific fields: Applied and Theoretical Electromagnetics, Instrumentation and Measurement, Power Engineering, Power Systems, Electrical Machines, Electrical Drives, Electronics, Telecommunications, Computer Engineering, Automatic Control and Systems, Mechatronics, Electrical Materials, Information Technologies, Engineering Mathematics, etc.
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