| Induction motors are widely used in modern industry due to their high reliability and convenient maintenance.After the emergence of Vector Control（VC）technology,its performance has reached a new level.Frequency conversion speed regulation has a wide range of applications,and has obvious energy-saving effects.It shines in many fields and has become the mainstream of the electric drive direction.In the actual operation of the induction motor,various parameters will change with the complicated working conditions and external interference,which will inevitably affect the VC effect,and also hinder the control performance of the variable frequency speed regulation system.The parameter identification technology of induction motors has high theoretical research value and engineering practical value.The traditional offline identification experiment of induction motor is cumbersome and cannot avoid the blocking of external machinery.For this reason,this thesis adopts an improved offline identification method,replacing short-circuit experiment with single-phase experiment,through DC experiment,single-phase experiment,free-load experiment identifies the stator resistance Rs,rotor resistance Rr,stator leakage inductance Lls(rotor leakage inductance Llr),mutual inductance Lm respectively,and finally the motor parameters required by the speed control system are obtained.However,the parameters are obtained by offline identification change in real time during the operation of the motor,so online intelligent identification is very necessary.The identification accuracy of commonly used online algorithms is susceptible to interference.For this reason,this thesis adopts Ant Colony Optimization（ACO）,Particle Swarm Optimization（PSO）,and Genetic Algorithm（GA）with strong global optimization capability and high convergence accuracy.A comparative simulation experiment was carried out to verify the optimization ability and convergence accuracy of ACO.At the same time,combining the existing two classic models in theαβcoordinate system,analyzing their respective advantages,using different processing methods,established an improved model and two transformation models,respectively embedded in the ACO program,and carried out two more comparing simulation experiments,it is verified that the application of ACO in the improved model and two transformation models is feasible,effective and accurate.Based on the excellent effect of ACO identification of motor parameters,this thesis also considers applying ACO to variable frequency speed regulation system.Through starting experiment,sudden load experiment,constant torque speed sudden increase experiment and braking experiment,different working conditions are compared.The changes of flux,torque,and speed before and after the ACO identification module is embedded under the disturbance,verify that accurate parameter identification is of great significance for improving the performance of the speed control system.The starting point of this thesis is to combine existing theoretical methods to solve practical application problems.This thesis has 60 figures,9 tables and 76 references. |
