Abstract
In order to further improve the accuracy of parameter identification under the fluctuation of traction motor speed (torque) and improve the speed control performance of the motor, the vector control strategy of traction motor is optimized. An online identification model of motor parameters based on recursive least squares (RLS) and model reference adaptive method (MRAS) is proposed. The motor stator and rotor parameters identified by RLS are input into MRAS on the basis of the rotor flux observation model. A proportional-integral adaptive law by use of Popov’s hyperstability theory is designed to identify the rotor resistance. Through the above optimization, the vector control strategy is optimized to realize effective control of speed regulation characteristics of traction motors in different speed intervals and under different working conditions. Consequently, the effectiveness of the proposed model and control strategy is realized and verified by simulation.
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References
Buchholz O, Bocker J (2017) Gopinath-observer for flux estimation of an induction machine drive system. In: IEEE Southern Power Electronics Conference (SPEC), IEEE, Germany, pp 1–7
Jing T, Yong HY, Frede B, Jie C, Li JD, Zhi GL (2018) Parameter identification of inverter-fed induction motors: A review. Energies 11(9):2194
Morey MS, Virulkar VB, Dhomane GA (2016) MRAS based speed identification and online updating of rotor time constant for sensorless field oriented controlled induction motor. In: International Conference on Emerging Trends in Electrical, Electronics and Sustainable Energy Systems (ICETEESES), IEEE, India pp 179–185
Hong YL, Qun JW, Fang X (2015) Parameters estimation of IM with the extended Kalman filter and least-squares. In: IEEE 10th Conference on Industrial Electronics and Applications (CIEA), IEEE, New Zealand, pp 1625–1628
Ramin N, Mehdi A, Hossein V (2018) Improving performance of sensorless vector control using artificial neural network against parametric uncertainty. In: CPE-POWERENG, IEEE, Qatar, pp 1–6
Fahrner W, Vogelsberger MA, Wolbank T (2018) A new technique to identify induction machine rotor parameters during dynamic operation and low speed. In: IEEE 18th international Power Electronics and Motion Control Conference (PEMC), IEEE, Hungary, pp 471–476
Bhavnesh K, Trishla G (2018) Investigations on flux estimation methods for stator current based MRAS speed estimator for induction motor drive. In: IEEMA Engineer Infinite Conference (eTechNxT), IEEE, India pp 1–5
Aenugu M, Tejavathu R (2015) Rotor-flux based MRAS speed estimator for direct torque and flux control of an induction motor drive. In: IEEE Students Conference on Engineering and Systems (SCES), IEEE, India, pp 1–6
Jeong I, Gu BG, Kim J (2015) Inductance estimation of electrically excited synchronous motor via polynomial approximations by least square method. IEEE Trans Ind Appl 51(2):1526–1537
Arif I, Mohammed AH (2018) MRAS based sensorless control of induction motor based on rotor flux. In: International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES), IEEE, India, pp 152–155
Zhu QY, Chen JB, Cheng LK (2015) Simulation research on traction drive control system of high speed EMUs. Urban Rail Transit Res 18(2):19–23 (in Chinese)
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Tan, X., Xie, D., Zhu, Q., Li, Z., Dai, W., Wu, Q. (2020). Vector Control Optimization of Traction Motors Based on Online Parameter Identification. In: Jia, L., Qin, Y., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 638. Springer, Singapore. https://doi.org/10.1007/978-981-15-2862-0_49
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DOI: https://doi.org/10.1007/978-981-15-2862-0_49
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