Design Optimization of Large-Capacity High-speed Permanent-Magnet Generator Based on Surrogate-Assisted Non-dominated Sorting Genetic Algorithm

The large-capacity high-speed permanent-magnet generator, with its unique design, plays a crucial role in special applications such as aviation and integrated power system. However, it has strict requirements for power quality, and there is a problem of design optimization efficiency. In this article, a 2.5 MW, 7000 r/min large-capacity high-speed permanent-magnet generator is designed. To enhance the power quality and optimization efficiency, an efficient optimization method based on surrogate-assisted non-dominated sorting genetic algorithm II (SANSGA-II) is proposed. Initially, the surrogate model of the generator is constructed by using Latin hypercube sampling, and the relationship between optimization variables and objectives is explored. Subsequently, the surrogate model is utilized to predict and sort the objective values in the variable space. The variable values in the high-value area are taken as the adjustment directions for crossover and mutation, thereby improving the optimization efficiency and diversity of the algorithm. In addition, the dynamic point addition strategy is implemented. Then, the accuracy of the model is ensured and the reliability of the algorithm is enhanced. These improvements provide a theoretical basis for the optimization method of the generator. By using the proposed and traditional methods, the generator performance is optimized. The results show that the proposed method achieves faster iteration speed and higher solution quality than traditional method. Finally, the prototype is manufactured based on the design parameters and tested. The optimization results are validated for both accuracy and reliability.