A Computationally Efficient Algorithm for Rotor Design Optimization of Synchronous Reluctance Machines

作者：Mohammadi M H; Rahman T; Silva R; Li M; Lowther D A^*

来源：IEEE Transactions on Magnetics, 2016, 52(3): 8200804.

DOI：10.1109/TMAG.2015.2491306

• 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.

• 出版日期2016-3
• 单位McGill