Three-level Inverter And PMSM Vector Control Based On Ant Colony Algorithm

Three-level inverter,as the representative of multilevel inverter,has been widely applied in high voltage and large capacity applications.The PWM technology realizes the transformation function of converting DC to AC by controlling the conduction and shutdown state of switches.In this paper,the three phase three-level inverter is taken as the research object,focusing on the integration of SHEPWM strategy and ant colony algorithm.Aiming at the problem of low accuracy and slow speed in solving the switching equation of SHEPWM three level inverter,the real-time calculation of SHEPWM is realized by ant colony algorithm.The chaotic idea is introduced into ant colony algorithm,and the idea of idle ants is introduced on the basis of the chaotic ant colony algorithm to prevent the algorithm from stagnation effectively.At the same time,the variable scale chaos algorithm is used to make the optimal ant search around the region.The search radius is set in advance,and in the process of iteration,the optimization can be accelerated and the goal of global optimization will be obtained.Based on the intelligent algorithm,the nonlinear transcendental equation of the SHEPWM strategy is solved by the advantage of intelligent algorithm in the target optimization,and the switch angle can be obtained so accurately that the real-time control of the three level inverter will be realized by SHEPWM strategy.The real-time control of the SHEPWM strategy for the three level inverter is realized,and the load carrying capacity of the inverter is verified by the open loop drive simulation experiment of the motor.Using the SHEPWM three level inverter as the driving of the PMSM,the vector control system of the permanent magnet synchronous motor is studied on the basis of the drive system of the motor.In the vector control system of permanent magnet synchronous motor,the fuzzy PID controller based on ant colony optimization is introduced into the speed controller.Traditional ant colony fuzzy algorithm uses ant colony algorithm to optimize membership functions and fuzzy rules step by step.This method ignores the correlation between membership functions,fuzzy rules,quantification and scaling factors in fuzzy controllers.Meanwhile,when multiple parameters are optimized with the same ant colony,the initialization and pheromone content of each sub target can not be adjusted according to the needs of each sub-target.In this paper,the idea of co-evolution is used to set several ants colony to optimize the parameters of fuzzy controller respectively,which can effectively solve the problem of too many ants in each iteration of the complex system,and can improve the speed of convergence.By building the Matlab software platform,the example of Oliver30 in the TSP problem and step response control are simulated.The simulation results show that idle ant algorithm has faster convergence speed and better accuracy.Meanwhile,the three-level inverter based on the variable scale chaotic lazy ant algorithm and SHEPWM strategy is simulated.From the simulation results,it can be seen that the harmonic content of the output line voltage of the three-level inverter is obviously reduced,and the 5,7,11 and 13 harmonics in the line voltage are completely eliminated.The validity and accuracy of the switch angle transcendental equation calculation is demonstrated.The vector control system of permanent magnet synchronous motor based on fuzzy PID controller optimized by ant colony is established.The simulation results show the superiority of the controller in the speed control precision and dynamic response degree.