An approach for genetic algorithm aided design of superconducting generator

An optimal design approach of superconducting generator of 70 MW class capacity based on genetic algorithm for the purpose of optimization of efficiency and specific power density is described. As the efficiency and the specific power density are handled as the objectives, multiobjective technique is also introduced to find the trade-off or compromise solution between two objectives. The multiobjective technique is performed by modified min-max approach. The design results of multiobjective optimization have the compromise solution inclined to those of volume optimization because its design variables are inclined to those of volume optimization. Moreover, the design method used in this paper shows to be effective and suitable, compared with those of 70 MW class superconducting generator already developed by national project in Japan.