6533b7dafe1ef96bd126edc8

RESEARCH PRODUCT

A Saturation Model of the Synchronous Reluctance Motor and its Identification by Genetic Algorithms

Marcello PucciMaurizio CirrincioneAngelo AccettaAntonino Sferlazza

subject

IdentificationComputer simulationComputer scienceStator05 social sciences020207 software engineering02 engineering and technologylaw.inventionSettore ING-INF/04 - AutomaticaControl theorylawMagnetic characteristicParameters' estimationGenetic algorithm0202 electrical engineering electronic engineering information engineeringSuperimposition0501 psychology and cognitive sciencesMagnetic characteristicsSynchronous Reluctance Motor (SynRM)Synchronous reluctance motorSaturation (magnetic)050107 human factors

description

This paper proposes a complete saturation model of the Synchronous Reluctance Motor (Syn1231), accounting for both the self-saturation and cross-saturation effects. This model is based on an analytical relationship between the stator flux and current components, and is characterized by parameters presenting an interesting physical interpretation, differently from many other saturation model in the scientific literature. It proposes also an identification technique of such a model based on stand-still tests, without the need of locking the rotor. The proposed saturation model permits the complete description of the magnetic behaviour of the machine with 8 parameters, fewer than those required by other models in the scientific literature. Finally, the parameters of this model have been retrieved by a adopting Genetic Algorithm (GAs). The proposed identification technique has been tested in both numerical simulation and experimentally on a suitably developed test set-up. Experimental results clearly show a good superimposition between the measured stator flux components and those computed with the proposed saturation model, by using the identified parameters.

yearjournalcountryeditionlanguage
2018-09-012018 IEEE Energy Conversion Congress and Exposition (ECCE)
https://doi.org/10.1109/ecce.2018.8558250