Research On Performance Optimization Of Permanent Magnet Torque Motor Based On Kriging+PSO Algorithm

With the development of computer science and technology in today's era and the continuous improvement of numerical methods and theories of electromagnetic fields,the optimization of motors has become one of the hot issues in electrical engineering at home and abroad.In order to achieve the optimal design of the permanent magnet torque motors,this paper combines the Kriging interpolation method with particle swarm optimization(PSO)to study a fast and effective global optimization algorithm Kriging+PSO,which is different from the traditional random global optimization algorithm that needs to calculate the electromagnetic field in every iteration and the surface response model algorithm of fixed parametric polynomials.Instead,it adopts a semi-parametric approach to construct an approximate response model,and then a global scope search is performed.It not only overcomes the disadvantages of solving complex inverse problems of electromagnetic fields,such as occupying a large amount of computer memory and using more time.At the same time,the problem of falling into a loacal optimum is avoided,and the torque performance of the permanent magnet torque motor is further optimized.The specific work is as follows:First of all,this paper introduces the principle of permanent magnet torque motor and preliminary design of permanent magnet torque motor parameters.The finite element simulation was permformed using Maxwell,and the initial torque performance of the 80-pole,96-slot surface-mounted permanent magnet synchronous torque motor model was obtained.Then,in order to improve its interpolation accuracy and operating efficiency,PSO algorithm was used to optimize its variogram parameters in depth Kriging method and its principles,and a Kriging+PSO interpolation estimation method was constructed to construct the Kriging interpolation model.Finally,through multiple iterations,the PSO method is used again for global optimization to get the best point.Secondly,this paper uses Matlab Software to program the new algorithm and verify the feasibility of the algorithm through two test functions.Furthermore,The numerical simulation results show that the Kriging model with PSO optimized for the relevant parameters has high interpolation accuracy.Compared with the pattern search method used in DACE,PSO is not affected by the initial solution,and not easy to fall into local optimums.Finally,taking the notch width,pole-arc coefficient,and permanent magnet thickness as independent variables,their influences on the cogging torque and the average torque were discussed respectively,and multi-objective optimization problems were introduced.The multi-objective global optimization of the permanent magnet torque machinery model is performed and compared with the initial model.