Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

Martin Nell, Alexander Kubin and Kay Hameyer
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Martin Nell: Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany
Alexander Kubin: Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany
Kay Hameyer: Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany

Energies, 2021, vol. 14, issue 17, 1-24

Abstract: 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: induction machine; electromagnetic models; model selection; optimization; artificial neural networks; pattern search; evolutionary strategy; simulated annealing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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