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Analytical Iron Loss Evaluation in the Stator Yoke of Slotless Surface-Mounted PM Machines.
- Source :
- IEEE Transactions on Industry Applications; Jul/Aug2022, Vol. 58 Issue 4, p4602-4613, 12p
- Publication Year :
- 2022
-
Abstract
- One of the attractive benefits of slotless machines is low losses at high speeds, which could be emphasized by a careful stator core loss assessment, potentially available already at the predesign stage. Unfortunately, mainstream iron loss estimation methods are typically implemented in the finite element analysis environment with a constant coefficients dummy model, leading to weak extrapolations with huge errors. In this article, an analytical method for the iron loss prediction in the stator core of slotless PM machines is derived. It is based on the extension of the 2-D field solution over the entire machine geometry. Then, the analytical solution is combined with variable coefficient loss models (VARCO) or constant coefficient loss models, which can be efficiently computed by vectorized postprocessing. VARCO loss models are shown to be preferred at a general level. Moreover, this article proposes a lookup-table-based solution as an alternative approach. The main contribution lies in the numerical link between the analytical field solution and the iron loss estimate, with the aid of a code implementation of the proposed methodology. First, the models are compared against a sufficiently dense dataset available from laminations manufacturer for validation purposes. Then, all the methods are compared for the slotless machine case. Finally, the models are applied to a real case study and validated experimentally. [ABSTRACT FROM AUTHOR]
- Subjects :
- STATORS
IRON
FINITE element method
MACHINERY
LAPLACE'S equation
CRASH test dummies
Subjects
Details
- Language :
- English
- ISSN :
- 00939994
- Volume :
- 58
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industry Applications
- Publication Type :
- Academic Journal
- Accession number :
- 158186211
- Full Text :
- https://doi.org/10.1109/TIA.2022.3171528