Research On Switched Reluctance Motor Modeling And Noise Reduction Strategy

Switched reluctance motors(SRM) are gradually replacing the market share of the traditional motor with its simple structure, reliable operation, starting torque, excellent speed performance and other advantages. Switched reluctance motor is used in household appliances, electric vehicles, aerospace, coal drilling driving and other fields. However, there are also drawbacks such as modeling, large torque ripple and noise. Aiming at the problems existing in SRM, the main work is as follows:First, Finite Element analysis has been used to clear the electromagnetic field distribution and obtain the relationship of flux, current, and torque data when the motor is running, which provides theoretical basis for modeling and noise reduction strategy.Improving the lack of the standard BP neural network. This algorithm is applied to the nonlinear modeling of SRM, so that we can get a nonlinear model. And the model is applied to the control system. The simulation results show that the model is more accurate than the traditional linear model and the control system has better performance.In the noise modeling of SRM. Firstly, analyzing the source of noise. Then using the theory of sound radiation, the electromechanical analogy method and Maxwell stress method to get the analytical model of the noise, the model shows that the parameters such as rotor pole arc, winding turns, air gap length and so on can be optimized to reduce the SRM noise. According to the positive correlation between the noise and radial force, the three dimensional finite element method is applied to simulate the radial force and obtain the radial force waveform when the motor is running and demonstrates the correctness of the analytical model.From the perspective of torque control for noise reduction. Aiming at the deficiency of the traditional open loop control of torque, Iterative Learning algorithm is applied to the torque control and current control of SRM, by this we design a closed loop torque control system based on iterative learning, with the application of a cubic form the torque distribution function, the noise of the motor has been reduce.The motor simulation model is established in MATLAB/Simulink and the nonlinear model obtained above is applied to the simulation. The structure of torque compensator, current controller and other modules is given in detail. Then building the hardware platform of SRM. The simulation results and experimental results show that the control method can effectively reduce the torque ripple, so as to achieve noise reduction.