Injection based sensorless performance optimization of surface mounted permanent magnet motor using Particle Swarm

This paper shows the results of an intention to design of surface mounted permanent magnet (SMPM) machines with self-sensing capabilities determining design parameters by Particle Swarm Optimization (PSO). A methodology will be presented which will look at the use of PSO to close up the torque to the highest values as possible and to maximize the self-sensing properties of such machines. A PSO environment has been combined with a finite element analysis (FEA) environment to enable the designer to account for both geometrical and saturation saliencies for an effective determination of the machine's self-sensing characteristics. The results obtained are satisfactory in terms of torque maximization and self-sensing capability. The sensitivity of the major geometrical parameters of the machine investigated, as well.