Research On Five-parameters Identification Method Of Permanent Magnet Synchronous Motor Based On Improved Particle Swarm Algorithm

With the rapid development of permanent magnet materials,motor control technology and power electronics technology,permanent magnet synchronous motors are in a leading position compared with other motors in terms of volume,weight,torque and power density.These advantages have made them widely used in industry applications.The motor control system requires to obtain electrical and mechanical parameters in real-time.However,when the motor runs for a long time or the operating conditions change suddenly,its internal environment(such as temperature,magnetic field,etc.)will change,and these changes cause the motor parameters change in turn,which affects the performance of the motor control system.Therefore,it is of great significance to identify the parameters of the motor.The main research contents of this article are as follows.1.Introduce the research background and significance of the research.The parameter identification theory,core problems,and domestic and international research status of permanent magnet synchronous motors are introduced.The mathematical models,coordinate transformation,vector control and space vector pulse width modulation principle in three coordinate systems(ABC three-phase stationary coordinate system,?-?two stationary coordinate systems,and dq synchronous rotating coordinate system)are analyzed in detail.2.Design of the five-parameter identification model of the motor.Under the steady-state condition of motor vector control i_d=0(the rotation speed and torque are constant),the full-rank discrete equations of motor parameters(stator winding resistance,ac-axis inductance,permanent magnet flux linkage and moment of inertia)are designed for intelligent algorithm identification by injecting negative sequence weak magnetic current strategy into the d-axis of stator.3.Dead zone voltage compensation.The causes of the dead zone effect of the voltage source inverter are analyzed in detail,and a simple and effective dead zone error voltage compensation strategy is proposed,that is,the average value of the d and q axis voltages of the actual motor is calculated first,or the complex Simulate the average voltage,and then calculate the difference between it and the simple simulation model voltage,this difference is the dead zone compensation voltage of the motor identification model.4.Improve the particle swarm algorithm.The advantages and disadvantages of the basic particle swarm optimization algorithm(PSO)are analyzed in detail,and an initial parameter optimization particle swarm optimization algorithm(OPSO)using PSO to optimize its learning factors and inertia weight coefficients is proposed.On the basis of OPSO,a random mutation optimization particle swarm optimization algorithm(RVOPSO)is proposed.The algorithm strategy is to generate new particles based on random mutation based on individual optimal particles and global optimal particles in the population,and calculate them through the fitness function.The new particle fitness value is compared with the former.If the new particle fitness value is less than the former,the former is replaced,otherwise it is eliminated.5.Simulation and experimental verification.In the MATLAB/SIMULINK simulation platform and experimental platform,the feasibility of the five-parameter identification model and the effectiveness of the improved particle swarm optimization performance strategy were verified.