Analysis and Research on Mechanical Stress and Multiobjective Optimization of Synchronous Reluctance Motor
| Autor: | Han Zhou, Xiuhe Wang, Lixin Xiong, Xin Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | CES Transactions on Electrical Machines and Systems, Vol 8, Iss 3, Pp 274-283 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2096-3564 2837-0325 |
| DOI: | 10.30941/CESTEMS.2024.00015 |
| Popis: | The mechanical strength of the synchronous reluctance motor (SynRM) has always been a great challenge. This paper presents an analysis method for assessing stress equivalence and magnetic bridge stress interaction, along with a multiobjective optimization approach. Considering the complex flux barrier structure and inevitable stress concentration at the bridge, the finite element model suitable for SynRM is established. Initially, a neural network structure with two inputs, one output, and three layers is established. Continuous functions are constructed to enhance accuracy. Additionally, the equivalent stress can be converted into a contour distribution of a three-dimensional stress graph. The contour line distribution illustrates the matching scheme for magnetic bridge lengths under equivalent stress. Moreover, the paper explores the analysis of magnetic bridge interaction stress. The optimization levels corresponding to the length of each magnetic bridge are defined, and each level is analyzed by the finite element method. The Taguchi method is used to determine the specific gravity of the stress source on each magnetic bridge. Based on this, a multiobjective optimization employing the Multiobjective Particle Swarm Optimization (MOPSO) technique is introduced. By 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 optimized. The relationship between the optimization objectives and the design parameters can be obtained based on the response surface method (RSM) to avoid too many experimental samples. The optimized model is compared with the initial model, and the optimized effect is verified. Finally, the temperature distribution of under rated working conditions is analyzed, providing support for addressing thermal stress as mentioned earlier. |
| Databáze: | Directory of Open Access Journals |
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