Multi-objective Optimization Structure Design Of Switched Reluctance Motor Based On Hybrid Genetic Algorithm

Switched reluctance motors(SRM)have been widely used in many fields due to their simple structure,low cost and reliable system.However,large torque ripple is a major problem that limits the further development of SRM.At present,there are two main aspects for the research on suppressing torque ripple.One is to analyze the relationship between motor structure and output performance,and to optimize the design by using optimization method.Secondly,the new control strategy is studied and analyzed.In order to reduce SRM torque ripple and improve output performance,this dissertation elaborates the influence of motor structure on performance,and uses hybrid genetic algorithm to conduct multi-objective optimization of SRM structure.This dissertation comprehensively summarizes the development history of SRM and genetic algorithm and their current research status at home and abroad.Aiming at the cumbersome calculation process and the single optimization direction of the traditional motor optimization method,as well as the premature convergence and stochastic oscillation of simple genetic algorithm,combined with the simulated annealing algorithm,the hybrid genetic algorithm converges faster and the efficiency is higher,which is proved by Rosenbrock's valley function.In this dissertation,an SRM prototype with a rated power of 2kW and a rated speed of 4000rpm is designed according to the traditional motor design method.Since ANSYS Maxwell is a powerful software mainly used for motor electromagnetic field analysis and optimization,this dissertation establishes SRM 2D and 3D finite element models based on ANSYS,and obtains the flux linkage characteristics of the prototype.According to the basic design parameters of the prototype and the finite element simulation results,the physical object of the motor was fabricated and the characteristics of the flux linkage of the prototype were preliminarily detected by indirect measurement.Through the comparison of results,the accuracy of 3D finite element simulation is verified and used to test the performance of the motor after optimization.In order to further optimize the structure of SRM,this dissertation studies different types of stator and rotor teeth and determines that the torque ripple of the T-type stator tooth structure is significantly smaller.In this dissertation,the SRM mathematical model is established based on the equivalent magnetic circuit method.The static characteristic equation of the motor is obtained by nonlinear magnetic circuit calculation.Based on this,the radial force equation is established by Maxwell's stress method and the radial force,output efficiency and copper consumption are optimized respectively.The multi-objective optimization is carried out by using hybrid genetic algorithm with the structural parameters such as stator yoke height,stator pole arc,rotor pole arc and rotor outer diameter as optimization variables.The results show that the motor output performance is improved.Through three-dimensional finite element simulation,the torque ripple of SRM is obviously suppressed,and the result also proves the correctness of the proposed optimization method.In order to further suppress the SRM torque ripple,this dissertation uses the multi-objective optimization result to establish the closed-loop instantaneous torque control system for SRM based on BP neural network,and further suppresses the torque ripple in the control strategy.