LAPSE:2023.19953
Published Article
LAPSE:2023.19953
Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection
Martin Nell, Alexander Kubin, Kay Hameyer
March 9, 2023
Optimization methods are increasingly used for the design process of electrical machines. The quality of the optimization result and the necessary simulation effort depend on the optimization methods, machine models and optimization parameters used. This paper presents a multi-stage optimization environment for the design optimization of induction machines. It uses the strategies of simulated annealing, evolution strategy and pattern search. Artificial neural networks are used to reduce the solution effort of the optimization. The selection of the electromagnetic machine model is made in each optimization stage using a methodical model selection approach. The selection of the optimization parameters is realized by a methodical parameter selection approach. The optimization environment is applied on the basis of an optimization for the design of an electric traction machine using the example of an induction machine and its suitability for the design of a machine is verified by a comparison with a reference machine.
Keywords
artificial neural networks, electromagnetic models, evolutionary strategy, induction machine, model selection, Optimization, pattern search, simulated annealing
Subject
Suggested Citation
Nell M, Kubin A, Hameyer K. Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection. (2023). LAPSE:2023.19953
Author Affiliations
Nell M: Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany [ORCID]
Kubin A: Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany
Hameyer K: Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany
Journal Name
Energies
Volume
14
Issue
17
First Page
5537
Year
2021
Publication Date
2021-09-04
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14175537, Publication Type: Journal Article
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LAPSE:2023.19953
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doi:10.3390/en14175537
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