Analysis and Research on Mechanical Stress and Multiobjective Optimization of Synchronous Reluctance Motor

作     者：Han Zhou Xiuhe Wang Lixin Xiong Xin Zhang Han Zhou;Xiuhe Wang;Lixin Xiong;Xin Zhang

作者机构：School of Electrical EngineeringShandong UniversityJinan 250061China School of Electrical and Electronic EngineeringShandong University of TechnologyZibo 255000China Shandong Kehui Power Automation Co.Zibo 255000China 

出 版 物：《CES Transactions on Electrical Machines and Systems》 (中国电工技术学会电机与系统学报（英文）)

年 卷 期：2024年第8卷第3期

页      面：274-283页

核心收录：

学科分类：080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China under grant 52077122 and by the Taishan Industrial Experts Program 

主　　题：Multiobjective optimization Neural network Stress equivalence Synchronous reluctance motor Taguchi method 

摘      要：The mechanical strength of the synchronous reluctance motor(SynRM)has always been a great *** paper presents an analysis method for assessing stress equivalence and magnetic bridge stress interaction,along with a multiobjective optimization *** the complex flux barrier structure and inevitable stress concentration at the bridge,the finite element model suitable for SynRM is ***,a neural network structure with two inputs,one output,and three layers is *** functions are constructed to enhance ***,the equivalent stress can be converted into a contour distribution of a three-dimensional stress *** contour line distribution illustrates the matching scheme for magnetic bridge lengths under equivalent ***,the paper explores the analysis of magnetic bridge interaction *** optimization levels corresponding to the length of each magnetic bridge are defined,and each level is analyzed by the finite element *** Taguchi method is used to determine the specific gravity of the stress source on each magnetic *** on this,a multiobjective optimization employing the Multiobjective Particle Swarm Optimization(MOPSO)technique is *** taking the rotor magnetic bridge as the design parameter,ten optimization objectives including air-gap flux density,sinusoidal property,average torque,torque ripple,and mechanical stress are *** relationship between the optimization objectives and the design parameters can be obtained based on the response surface method(RSM)to avoid too many experimental *** optimized model is compared with the initial model,and the optimized effect is ***,the temperature distribution of under rated working conditions is analyzed,providing support for addressing thermal stress as mentioned earlier.