USE OF KALMAN FILTER IN VECTOR SYSTEM OF EXTREME CONTROL OF ASYNCHRONOUS MACHINE

In the article the reactive power of an asynchronous machine (AM) in a stable mode is obtained as a function of three vari-ables: the angular speed of rotation of the AM shaft, the module of the rotor flux coupling and the moment of static load on the shaft. The AM power factor decreases in electric drives (ED) of industrial mechanisms in which the load moment while maintaining its direction of action can take a static value less than the nominal value. Therefore, in order to ensure a high power factor of an asynchronous ED in long-term operating modes with a variable load, a vector AM control system was created, in which the magnitude of the rotor flux coupling is regulated by the reactive power channel as a function of the mo-ment of static load on the shaft. To identify the moment of loading, as well as the angular speed of the rotor and its flux cou-pling, a Kalman observer is synthesized, which allows creating a vector control system with simultaneous regulation of the speed and power factor of the AM without physical sensors of the reference vector of the rotor flux coupling and its angular speed of rotation. The significant dependence of the extreme values of the rotor flux coupling for reactive power on the torque on the AM shaft and insignificant dependence on the speed has been proved. Stability and high control quality are achieved by simultaneous use of relay control laws, invariant to coordinate and parametric perturbations, and the effectiveness of the Kalman filter algorithm for identifying variable states in the feedback loop. Thus, the article theoretically substantiates the idea of creating a sensorless relay-vector control system for an asynchronous ED with simultaneous speed regulation and optimization of AM energy indicators. The mathematical model is created as a program written in the Matlab programming language. The operability of the proposed asynchronous ED control system was confirmed by the method of mathematical modeling. References 12, figures 6.