@inproceedings{26e14adc3e464094b8a4cca776f43bfc,
title = "Injection based sensorless performance optimization of surface mounted permanent magnet motor using Particle Swarm",
abstract = "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.",
keywords = "Design optimization, Particle swarm method, Sensorless control performance, Surface mounted permanent magnet machine",
author = "M. Caner and C. Gerada",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017 ; Conference date: 20-04-2017 Through 21-04-2017",
year = "2017",
month = jun,
day = "13",
doi = "10.1109/WEMDCD.2017.7947732",
language = "English",
series = "Proceedings - 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "107--112",
booktitle = "Proceedings - 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017",
address = "United States",
}