Multiobjective Optimization Design of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Using NSGA-II

In order to improve the performance (low torque ripple, low suspension ripple, and high power factor) of the permanent magnet assisted bearingless synchronous reluctance motor (PMa-BSynRM), the multiobjective optimal design based on fast nondominated sorting genetic algorithm (NSGA-II) of PMa-BSynRM rotor topology is investigated in this article. First, the structure and operation principle of the PMa-BSynRM are introduced. Second, the initial rotor design that gives optimizer the ability to produce a variety of barrier shapes and the comprehensive sensitive analysis that evaluates the influence of each design variable on optimization objectives are presented. Third, the optimal design is selected from the Pareto front, which is generated by NSGA-II, and validated by finite-element analysis. The simulation results confirm that the torque and suspension force capacity of optimal motor are improved significantly in comparison with initial design, whereas the value of power factor reaches 0.81. Finally, the optimal prototype motor is manufactured, and experimental results confirm the validity and superiority of the optimized design.

Automated summary

This article investigates multiobjective optimal design based on NSGA-II for improving the performance of PMa-BSynRM, through sensitivity analysis and finite element analysis, a method to enhance the performance of the motor is proposed and validated.