Asynchronous Motor Paremeters Self-Commission And Adaption Research On Vector Control System

By decoupling between magnetizing current and torque current, the vector control strategy of induction machines can achieve excellent dynamic performances. In order to achieve high-performance vector control system and improve the robustness of the system, it is needed to identify the motor parameters. This paper will explore how to achieve high performance vector control system on experimental platform with motor parameters self-commission and adaptive, and with detailed study of the parameter identification performance.Three-phase asynchronous motor mathematic model is first introduced. The principles and methods of motor parameters self-commission and rotor resistance adaptive are elaborated.The simulation analysis of parameter self-commission algorithm verificats the correctness and feasibility of this method. TMS320F2812 DSP is selected for control chip of the system, the hardware circuit is designed. The Software is composed according to the control algorithm and the hardware circuit. Space vector PWM control method is studied, and VVVF motor control algorithm is achieved. The FIR digital filter is design to filter sampling current , and mean while the influence of dead time and voltage drop on identification is analysed. A simple compensation is implemented which improves the accuracy of the parameter identification. Experimental results show that the self-commission algorithm has a high accuracy of the identification. The identification of different motors further verifies the validity of the method.This paper proposes a new method of rotor resistance online identification. The identification result is not affected by stator resistance. At the same time, the other two identification methods of model reference adaptive system (MRAS) are compared, simulation results show that this method has a good dynamic performance, robustness and affected small by torque and speed. A detailed simulation analysis of this method is carried out in a variety of conditions, and the algorithm is finally achieved in DSP. Simulation and experimental results show that the method has a high steady-state accuracy and fast speed of convergence, the programme has verified the correctness and feasibility of this method.