Optimal Design of Water-Filled Submersible PMSM for High Efficiency

This study presents an improved genetic algorithm (IGA) and uses it to optimize the design of a water- filled submersible permanent magnet synchronous motor (PMSM) to increase efficiency. First, the loss composition of the water-filled submersible PMSM was analyzed, and an efficiency model was created. Second, an improved genetic algorithm (IGA) was proposed to address the issues of low search efficiency and local convergence of the simple genetic algorithm (SGA). The IGA introduced elite selection in the crossover operation and adaptively changed the crossover probability and mutation probability according to the population and individual situation. Finally, IGA was used to optimize the motor design parameters, and the results before and after optimization were compared through finite element simulation. The optimization method proved beneficial, as seen by the 1.8% improvement in efficiency.