State estimation accuracy analysis of an induction electric drive by the algorithms of Luenberger and Kalman

Bukreev V.G., Shandarova E.B., Perevoshchikov F.V. State estimation accuracy analysis of an induction electric drive by the algorithms of Luenberger and Kalman. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 1, pp. 35–43 (in Russian). doi: 10.17586/2226-1494-2023-23-1-35-43

In complex electromechanical objects containing electric drives with induction motors, it is often difficult or impossible to install sensors of output variables. In this case, to determine the output coordinates of the motor, it is necessary to introduce state observers into the control system of the electric drive. The main problems of creating observers are the presence of noise and interference in the measuring circuits of the control system which affect the accuracy of the estimation of immeasurable state variables. The paper presents a comparison of the accuracy of estimates obtained as a result of the work of the observer algorithms based on the Kalman filter and the observer of the Luenberger in the induction electric drive system, with vector control at the noise level of the current measurement channels in the stator windings of the induction motor. To synthesize algorithms for state observers, methods of identification theory and quasi-linearization of nonlinear models of the control object under consideration were used. The simulation model of an induction motor is based on a classical vector field-oriented control system where an estimate of the angular speed of the motor shaft is used as a feedback signal. The model implements the following blocks: a mathematical model of an induction motor in a two-phase fixed coordinate system α–β; the structure of the observer algorithm; the procedure for converting the basis of the current vector and the control voltage from stationary to rotating and vice versa; proportional-integral regulators of current, flux linkage and angular speed. The S-shaped intensity setter forms a speed setting curve. The input signals for observers are the stator voltages and currents of the reference model of an induction motor. The adaptation coefficients for the Luenberger observer were selected experimentally from the condition of obtaining the average minimum value of the difference modulus of the estimated values. The covariance matrices for the observer based on the Kalman filter are configured on the basis of the experiment, ensuring a minimum of the average value of the absolute error. The time dependences of the transients of the angular speed of the shaft, the modulus of the flux linkage vectors of the rotor and stator currents are obtained. The dependencies were evaluated when starting an induction motor with nominal values and values of frequency and voltage amounting to 10 % of the nominal values. The work of estimation algorithms in the presence of a noise component, as well as when changing the parameters of the induction motor replacement circuit by ± 10 %, is investigated. The results of modeling the operation of the electric drive in starting modes with a mechanical load equal to the nominal value at a supply voltage frequency of 50 Hz and at 10 % of the nominal value for a voltage of 1 Hz are obtained. It is shown that the greatest relative estimation errors occur in the starting mode of the electric drive, and the maximum accuracy is achieved in the case of using a nonlinear Kalman filter. The results of the work can be used in the development of automatic control systems for sensorless electric drives and frequency-controlled electric drive of centrifugal pumping units for oil production.

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