MATHEMATICAL MODELLING OF TECHNOLOGICAL PROCESSES AND SYSTEMS
A new high-quality dynamic identification structure for im parameters
- 1 Technical University of Kosice, Slovakia
Abstract
In the presented work a new identification method of difficult measured internal quantities of IM, such as components of magnetic flux vector and electromagnetic torque, is proposed. Commonly measurable quantities of IM like stator currents, stator voltage frequency and mechanical angular speed are used for identification to determine a feedback effect of the rotor flux vector on vector of stator currents of IM. Stability of the identification structure is guaranteed by position of roots of characteristic equation of its linear transfer function. Results obtained from simulation in MATLAB measurements confirm quality, effectivity, feasibility, and robustness of the proposed identification method.
Keywords
References
- BOSE, B. K.: Modern Power Electronics and AC Drives, Prentice-Hall: Englewood Cliffs, NJ, USA, 2002.
- BRANDSTETTER, P. – KUCHAR, M. – VO, H. H. – DONG, C.S.T.: Induction motor drive with PWM direct torque control, in Proc. 2017 18th Int. Scientific Conf. on Electric Power Eng., EPE 2017, Kouty nad Desnou, Czech Republic, pp. 1–5, 2017.
- ZHANG, Y. – BAI, Y. – YANG, H.: A universal multiple-vector-based model predictive control of induction motor drives, IEEE Trans. Power Electron., vol. 33, pp. 6957–6969, 2018.
- FEDOR, P. – PERDUKOVÁ, D. - KYSLAN, K. – FEDÁK, V.: Stable and robust controller for induction motor drive, IEEE 18th Int. Conf. on Power Electronics and Motion Control, PEMC 2018, Budapest, pp. 764-769, 2018.
- WALLSCHEID, O. – SCHENKE, M. – BÖCKER, J.: Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines, in 21st Int. Conf. on Electrical Machines and Systems, ICEMS-2018, Jeju, South Korea, 2018.
- VENKADESAN, A. – HIMAVATHI, S. – MUTHURAMALINGAM, A.: Performance comparison of neural architectures for on-line flux estimation in sensor-less vector-controlled IM drives, Neural Comput. & App., vol. 22, pp. 1735–1744, 2013.
- CHEN, K.Y. – YANG, W.H. – FUNG, R.F.: System identification by using RGA with a reduced-order robust observer for an induction motor, Mechatronics, vol. 54, pp. 1- 15, 2018.