期刊
IEEE TRANSACTIONS ON MAGNETICS
卷 52, 期 3, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMAG.2015.2491306
关键词
AC motor design; finite-element analysis (FEA); genetic algorithm; neural network; Pareto optimization; surrogate modeling
A generalizable algorithm is proposed for the design optimization of synchronous reluctance machine rotors. Single-barrier models are considered to reduce the algorithm's computational complexity and provide a relative comparison for rotors with different slots-per-pole combinations. Two objective values per sampled design (average and ripple torques) are computed using 2-D finite-element analysis simulations. Non-linear regression or surrogate models are trained for the two objectives through a Bayesian regularization backpropagation neural network. A multi-objective genetic algorithm is used to find the validated Pareto front solutions. An analytical ellipse constraint is then suggested to encapsulate optimal solutions. Compared with a direct sampling approach, this restriction captures an optimal region within the double-barrier space for further torque ripple reduction.
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