Study On Multi-objective Optimization Of Switched Reluctance Motors Based On FOA

With the advantages of simple structure,large starting torque,small starting current,wide speed range and reliable performance,switched reluctance motor has great potential for applications in the areas of electric cars.However,it also has some disadvantages,such as high torque ripple,low efficiency and large noise,which affect its popularization and application.Therefore,the structure parameters of switched reluctance motor are optimized at home and abroad,and the optimization is used to optimize the structure parameters,such as two surface response method,particle swarm optimization and genetic algorithm,etc.Although some achievements have been achieved,there are still shortcomings,such as the complexity of the algorithm,the low efficiency of regulation,the large amount of calculation and the possibility of falling into the local optimal solution.According to the above problem,the fruit fly algorithm and improved fruit fly algorithm were proposed to achieve good optimization results.The main research work of the paper includes:Firstly,the basic characteristics and development of switched reluctance motor were introduced briefly,then the paper describes the situation and existing problems of the optimization design of switched reluctance motor and introduces the research status and advantages of fruit fly algorithm.Multi-objective optimization design of switched reluctance motor is proposed by using the fruit fly algorithm.An Multi-objective optimization of switched reluctance motors based on fruit fly algorithm was proposed.The modeling method and optimization design method was described and compared with the traditional particle swarm optimization algorithm.The result proves the effectiveness of the optimization method of fruit fly algorithm.It is easy to jump out of the limit boundary when the fruit fly algorithm optimizes the pole arc of Switched Reluctance Motor.Also,in fruit fly algorithm,fixed step size is sensitive to search and difficult to select.To solve the above problems,an improved double population Drosophila algorithm is proposed.The optimization steps and pseudo code of this algorithm are described in this paper,and compared with the fruit fly algorithm.It is proved that the algorithm has better global search ability and higher search efficiency.Fruit fly algorithm has the problem of slow convergence and is easy to fall into the local optimal solution,when optimizing the structural parameters of switched reluctance motors.therefore a multi-objective optimization method based on chaotic fruit fly algorithm is proposed.The modeling and the optimization process of the method are described,and the simulation verification of the optimization effect is carried out.When the switched reluctance motor adopts the adaptive chaotic fruit fly algorithm(Logistic map-fruit fly algorithm),it has the problem of poor stability.Therefore,Levy flight was introduced into chaos fly algorithm to improve stability.In this paper,the optimization steps and pseudo code of Logistic map-Levy flight-fruit fly algorithm arediscussed,and the simulation verification of the optimization effect is carried out.It is proved that the algorithm has the characteristics of high stability and is not easy to fall into the local optimal solution.