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 multi-objective optimal design based on NSGA-II (fast non-dominated sorting genetic algorithm) of PMa-BSynRM rotor topology is investigated. Firstly, the structure and operation principle of the PMa-BSynRM are introduced. Secondly, 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. Thirdly, 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, while 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.

An inverter-fed six-pole radial hybrid magnetic bearing (HMB) has the characteristics of compact structure, high support speed, long service life and so on. However, using displacement sensors to detect rotor displacements leads to the problems of large volume, high cost and low reliability. In this paper, a self-sensing method using improved particle swarm optimization (IPSO) least square support vector machine (LS-SVM) is proposed, which eliminates the influences of displacement sensors fundamentally. The structure and working principle of six-pole radial HMB are introduced, and the mathematical model of its radial suspension force is deduced according to the equivalent magnetic circuit method. Based on the regression principle of the LS-SVM, the prediction model between the currents in control coils and the rotor displacements is established. And the performance parameters of LS-SVM are optimized by the IPSO algorithm, which realizes self-sensing modeling of rotor displacement. The simulation system for self-sensing modeling of rotor displacement for six-pole radial HMB is constructed, and floating experiment, static suspension experiment, dynamic suspension experiment and disturbance experiment of the rotor are carried out, which verify the robustness and stability of the self-sensing method proposed.

In order to realize the design objectives of high power generation performance and stable suspension capability, a multi-objective optimization method based on a response surface (RS) model and an improved multi-objective particle swarm optimization (MOPSO) algorithm is proposed and utilized to the multi-objective optimization design of a bearingless permanent magnet synchronous generator (BPMSG). Firstly, the operating principle and the mathematical model of the BPMSG are introduced. Secondly, the design variables and the design objectives are determined and the design space is reduced by the sensitivity analysis. Thirdly, the RS models of design objectives are constructed and the improved MOPSO algorithm is applied to get the Pareto optimal sets. Finally, the initial generator and the optimal generator are compared using the finite element analysis software. Compared with the initial generator, the average suspension force of the optimal generator is increased by 21% and the suspension force ripple is decreased by 52%.