电工技术学报  2025, Vol. 40 Issue (4): 1009-1022    DOI: 10.19595/j.cnki.1000-6753.tces.240207
电机及其系统 |
基于多物理场的磁悬浮轴承系统自适应多目标优化设计
徐煜昊1, 王晓远1, 刘铭鑫2, 李娜1, 尉恬1
1.天津大学电气自动化与信息工程学院 天津 300072;
2.国家电网天津市电力公司 天津 300010
Adaptive Multi-Objective Optimization Design of Active Magnetic Bearings System Based on Multi-Physics
Xu Yuhao1, Wang Xiaoyuan1, Liu Mingxin2, Li Na1, Yu Tian1
1. School of Electrical and information Engineering Tianjin University Tianjin 300072 China;
2. State Grid Tianjin Electric Power Company Tianjin 300010 China
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摘要 随着磁悬浮轴承(AMB)在工业各领域的应用,其受到的关注与日俱增,而随着工业各领域技术的不断进步,对AMB的设计和优化也提出了更高的需求。该文提出一种基于多物理场的AMBs系统自适应多目标优化(MOO)方法,通过自适应MOO方法与多物理场分析方法相结合,解决两者分别存在的参数合理性和迭代收敛性问题。通过仿真与传统多目标优化方法对比证明了该方法的可行性。最后根据优化后的AMBs参数,搭建200 kW,30 000 r/min高速永磁同步电机(HPMSM)驱动下的AMBs系统验证了所提方法的有效性。
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关键词 磁悬浮轴承多目标优化高速永磁同步电机遗传算法    
Abstract:Active magnetic bearing is a widely used supporting component in the industrial field. With the arrival of the wave of economic recovery, the trend of “high-speed” and “high-efficiency” in the industrial field has become increasingly apparent, which undoubtedly puts higher requirements on the performance of supporting components such as active magnetic bearings. Among numerous active magnetic bearing optimization design methods, traditional finite element methods generally suffer from high model complexity and slow calculation speed. On the other hand, although traditional multi-objective optimization methods have improved computational speed, they are prone to convergence issues due to the algorithm being trapped in local optima. Therefore, after careful consideration, an adaptive multi-objective optimization method based on the multi-physics field is proposed.
Firstly, the electromagnetic design process of a high-speed permanent magnet synchronous motor provides the basic design process of an active magnetic bearings system. In addition, taking the twelve-pole radial active magnetic bearing and the concentric single-ring axial active magnetic bearing as examples, specific formulas related to the design process are provided.
Secondly, comprehensive optimization is carried out on NSGA-Ⅱ, and the optimized NSGA-Ⅱ is combined with multi-physics fields to achieve a cyclic optimization process of real-time variable range correction. During the sorting process, the improved “super dominated” sorting method is chosen. Different adaptive rules with non-dominated levels and iteration times as variables are proposed in the selection process, crossover and mutation process, and retention process to ensure population diversity and adaptability in optimization.
Then, based on the operating conditions of the active magnetic bearings system, four main constraint forms were proposed: volume constraint, stiffness constraint, rotor strength constraint of radial active magnetic bearing, and thrust disc strength constraint of axial active magnetic bearing. Furthermore, the electromagnetic force density, total loss of the active magnetic bearings system, and critical speed are regarded as optimization objectives.
Then, using hypervolume and inverted general distance as performance indicators, the two optimization methods are compared, taking the ZDT problem as an example. The results show that compared with traditional NSGA-Ⅱ, the adaptive multi-objective optimization method not only converges faster but also reduces the time complexity from O(3×1002) to O(300×lg300).
Finally, the results before and after optimization are compared through multi-physics field simulation, and an experimental platform is built to compare the parameter changes of each optimization objective. Taking radial active magnetic bearing as an example, the optimized displacement stiffness coefficient increases by 105.4% from 459 N/mm to 943 N/mm. At the same time, the current stiffness coefficient increases from 80.4 N/A to 146 N/A with an increase of 81.6%. The error between the measured and calculated values after optimizing the displacement stiffness coefficient is 5.8%, and the error after optimizing the current stiffness coefficient is 7.3%. The simulation and experimental results demonstrate that the proposed adaptive multi-objective optimization method can ensure global optimization and fast convergence in active magnetic bearings systems.
Key wordsActive magnetic bearings    multi-objective optimization    high-speed permanent magnet synchronous motor    genetic algorithm   
收稿日期: 2024-01-30     
PACS: TH133.3  
  TM351  
通讯作者: 徐煜昊 男,1993年生,博士研究生,研究方向为磁悬浮轴承优化设计及控制。E-mail: yuhao_xu@tju.edu.cn   
作者简介: 王晓远 男,1962年生,教授,博士生导师,研究方向为电机及其系统设计、电机电磁场分析及计算、永磁电机及其应用、特种电机设计等。E-mail: xywang62@tju.edu.cn
引用本文:   
徐煜昊, 王晓远, 刘铭鑫, 李娜, 尉恬. 基于多物理场的磁悬浮轴承系统自适应多目标优化设计[J]. 电工技术学报, 2025, 40(4): 1009-1022. Xu Yuhao, Wang Xiaoyuan, Liu Mingxin, Li Na, Yu Tian. Adaptive Multi-Objective Optimization Design of Active Magnetic Bearings System Based on Multi-Physics. Transactions of China Electrotechnical Society, 2025, 40(4): 1009-1022.
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