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.
Highlights
Optimization methods are increasingly used in the electromagnetic design and redesign process of electrical machines
Compared to the reference machine, the optimized geometry has a lower volume due to the shortened active length, but higher mean losses over the drive cycle. This results in a by 2.6% worse fitness. This is a consequence of the insufficient coverage of all possible degrees of freedom of the machine geometry by the seven optimization parameters, which leads to the fact that the reference machine cannot be completely reproduced
The advantages of two stochastic and one deterministic optimization method are combined by successively applying Simulated Annealing (SA), Evolution Strategy (ES) and Pattern Search (PS)
Summary
Optimization methods are increasingly used in the electromagnetic design and redesign process of electrical machines. A very high number of machine simulations can be required In this case, the FEM and other analytical methods can lead to very high computational effort. Both the application of the successive ES-PS optimization and the previously executed stage of SA improve the convergence behavior in this case In all these stages, direct machine models are used for electromagnetic modeling. This leads to a multi-stage optimization environment that combines the advantages of deterministic and stochastic optimization methods and those of direct and indirect model building. It can be used for the design process and the design optimization of IM
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