Top-level design pattern of PM-assisted synchronous reluctance machines

Abstract

The main challenge of the PM-assisted synchronous reluctance machine (PMASynRM) design is to determine numerous parameters for the requirement of multi-objectives. According to the top-level design concept, the optimization for PMASynRMs can be regarded as multi-parameter and multi-objective optimization problems (MOOPs). In this paper, the high-dimensional optimization problem is transformed into two low-dimensional optimization sub-problems. The analytical model algorithm has been established to solve the first sub-problem. Then, the optimization algorithms including the particle swarm optimization (PSO), the standard genetic algorithm (GA) with elitist strategy, and the pattern search (PS) are used for the second sub-problem. It is revealed that the optimization with PS algorithm is superior, in aspects of optimized machine performance and optimization efficiency, compared with that of PSO and GA algorithms. Furthermore, four PMASynRMs have been optimized with the developed process coupled 2D-FEA simulation, and significant performance improvement has been achieved after optimization. Finally, a 7.5kW@3 000r/min prototype machine is manufactured and tested to validate the top-level design pattern.