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Uni- and Bidirectional Flux Variation Loci Method for Analytical Prediction of Iron Losses in Doubly-Salient Field-Excited Switched-Flux Machines

Identifieur interne : 004B73 ( Main/Exploration ); précédent : 004B72; suivant : 004B74

Uni- and Bidirectional Flux Variation Loci Method for Analytical Prediction of Iron Losses in Doubly-Salient Field-Excited Switched-Flux Machines

Auteurs : Benjamin Gaussens [France] ; Emmanuel Hoang [France] ; Olivier De La Barriere [France] ; Jacques Saint-Michel [France] ; Philippe Manfe [France] ; Michel Lecrivain [France] ; Mohamed Gabsi [France]

Source :

RBID : Pascal:13-0306092

Descripteurs français

English descriptors

Abstract

Field-Excited Flux-Switching machines are doubly-salient machines with complex flux-density waveforms in the core. Hence, prediction of iron losses is difficult. In this article, we propose a method to determine flux loci in iron parts in an analytical manner, accounting for bidirectional field in back-irons of stator and rotor. The analytical model for flux-density prediction in the core at no-load was first validated with 2D FE simulations. Then measured iron losses on a prototype machine were used to calibrate an iron loss model. It was shown that in FE-SF machines rotor iron losses are not negligible and represent 28% of total iron losses. This model could be advantageously used in a design optimization procedure.


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<div type="abstract" xml:lang="en">Field-Excited Flux-Switching machines are doubly-salient machines with complex flux-density waveforms in the core. Hence, prediction of iron losses is difficult. In this article, we propose a method to determine flux loci in iron parts in an analytical manner, accounting for bidirectional field in back-irons of stator and rotor. The analytical model for flux-density prediction in the core at no-load was first validated with 2D FE simulations. Then measured iron losses on a prototype machine were used to calibrate an iron loss model. It was shown that in FE-SF machines rotor iron losses are not negligible and represent 28% of total iron losses. This model could be advantageously used in a design optimization procedure.</div>
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