Electromagnetic optimal design of a PMSG considering three objectives and using NSGA-III
Electromagnetic optimal design of a PMSG considering three objectives and using NSGA-III
This article presents the optimal design of a permanent magnet synchronous generator (PMSG). A finite element (FE) model is used to construct a metamodel, which afterward is utilized to define the objective function that models the PMSG. Kriging modeling is employed along with the design of experiments based on Latin hypercube sampling. The utilization of a surrogate model allows to speed up the optimization process while keeping the accuracy since they are developed from the FE analysis. On the other hand, it has been reported that the non-sorting genetic algorithm (NSGA) III is better than NSGA-II because it can solve multi- and many-objective optimization problems. This article demonstrates by numerical experiments that NSGA-III can be successfully used in the optimal design of PMSG with three objectives.
Finite element method (FEM), Kriging, genetic algorithms, optimization, permanent magnet synchronous generator (PMSG)
Hernandez, C.
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Lara, J.
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Arjona, M. A.
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Martinez, F. J.
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Moron, J. E.
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Escarela-Perez, R.
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Sykulski, J.
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September 2022
Hernandez, C.
ad3d192f-e69f-4c51-9eb0-5399904ca7cf
Lara, J.
d45379bb-a5eb-40ae-af65-9337ccffdeba
Arjona, M. A.
4938f9c4-2842-465a-9530-1578de8f5465
Martinez, F. J.
035c439e-6219-442e-a408-aa64f6369256
Moron, J. E.
241f9bf0-516d-47bc-8d08-b0f850c0a5f3
Escarela-Perez, R.
50190df0-7339-473b-91f5-5bd1c09ffed4
Sykulski, J.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Hernandez, C., Lara, J., Arjona, M. A., Martinez, F. J., Moron, J. E., Escarela-Perez, R. and Sykulski, J.
(2022)
Electromagnetic optimal design of a PMSG considering three objectives and using NSGA-III.
IEEE Transactions on Magnetics, 58 (9), [8107504].
(doi:10.1109/TMAG.2022.3167306).
Abstract
This article presents the optimal design of a permanent magnet synchronous generator (PMSG). A finite element (FE) model is used to construct a metamodel, which afterward is utilized to define the objective function that models the PMSG. Kriging modeling is employed along with the design of experiments based on Latin hypercube sampling. The utilization of a surrogate model allows to speed up the optimization process while keeping the accuracy since they are developed from the FE analysis. On the other hand, it has been reported that the non-sorting genetic algorithm (NSGA) III is better than NSGA-II because it can solve multi- and many-objective optimization problems. This article demonstrates by numerical experiments that NSGA-III can be successfully used in the optimal design of PMSG with three objectives.
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10.1109 TMAG.2022.3167306
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e-pub ahead of print date: 13 April 2022
Published date: September 2022
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© 2022 IEEE.
Keywords:
Finite element method (FEM), Kriging, genetic algorithms, optimization, permanent magnet synchronous generator (PMSG)
Identifiers
Local EPrints ID: 472508
URI: http://eprints.soton.ac.uk/id/eprint/472508
ISSN: 0018-9464
PURE UUID: 26de9fcf-443a-4f66-af4a-75269c94ee80
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Date deposited: 07 Dec 2022 17:42
Last modified: 17 Mar 2024 02:33
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Contributors
Author:
C. Hernandez
Author:
J. Lara
Author:
M. A. Arjona
Author:
F. J. Martinez
Author:
J. E. Moron
Author:
R. Escarela-Perez
Author:
J. Sykulski
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