Open AccessResearch on Optimal Design of Double Stator Low-speed High-torque Synchronous Motor Based on Surrogate Model
Author(s) -
Chen Yang,
Fengge Zhang,
Siyang Yu,
Shi Jin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1887/1/012047
Subject(s) - control theory (sociology) , surrogate model , torque ripple , torque , design of experiments , optimal design , stator , computer science , finite element method , sensitivity (control systems) , genetic algorithm , engineering , direct torque control , mathematics , induction motor , electronic engineering , mechanical engineering , structural engineering , physics , statistics , control (management) , voltage , machine learning , artificial intelligence , electrical engineering , thermodynamics
Optimizing double stator low-speed high-torque synchronous motor (DSLHSM) with many parameters is difficult and time-consuming, so an optimization design method suitable for multi-parameter problems was proposed. Through the method, the design optimization of DSLHSM can be more effective, and the optimization time is greatly shortened. Firstly, the optimization variables were selected after the sensitivity of the key parameters of average torque and torque ripple was analysed by using design of experiments (DOE) and analysis of variance (ANOVA). Secondly high-precision dynamic surrogate models of average torque and torque ripple were established based on response surface model (RSM), and then the objective function was established according to the surrogate model and the genetic algorithm (GA) as well as the sequential subspace optimization method (SSOM) was used to solve the objective function step by step. Finally, the optimal solution was substituted into the finite element simulation model. The target performances of the motor before and after optimization were compared and analysed. The results proved the effectiveness and correctness of the optimization design method.