Mezentsev N.V., Gejko G.V Identification parameters of traction induction motor of diesel train using genetic algorithms

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Abstract

Quality management system of asynchronous electric drive assumes knowledge of exact values of its parameters. However, some of the parameters (this applies mainly to the rotor axis) can not be directly obtained. This paper describes a mathematical algorithm based on which one can estimate the physical parameters of the traction induction motor by measuring the currents and voltages of the stator windings and the rotor speed. At the base of the algorithm is the mathematical model of an asynchronous traction drive with the limitations provided in the fixed reference frame (α, β, 0). An example of a mathematical model of the traction drive was given where the values, which could not be obtained directly, were excluded. Some parameters were determined using the method of least squares. To find the remaining parameters it was proposed to use a genetic algorithm. This approach can be used to construct an observer for the correction of the existing controls in the movement control system of traction rolling stock.

Keywords

Asynchronous traction drive, the method of least squares, genetic algorithm.

Mezentsev Nickolaj Viktorovich – Ph.D. (Eng.), Associate Professor, National Technical University "Kharkov Polytechnic Institute", Kharkov, Ukraine.

Gejko Gennadij Viktorovich – Assistant Professors, postgraduate student, National Technical University "Kharkov Polytechnic Institute", Kharkov, Ukraine. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it..

1. Dmitrienko V.D., Zakovorotnyj A.Ju. Modelirovanie i optimizacija processov upravlenija dvizheniem dizel-poezdov [Simulation and optimization of control processes of diesel-trains movement]. – Kharkov: Publishing center of "HTMT", 2013, 248 p.

2. Beshta A.S., Valahoncev A.V., Hudoj E.G. Identifikacija koordinat asinhronnogo dvigatelja v uslovijah drejfa aktivnyh soprotivlenij [Induction motor coordinate identification in case of active resistance drift]. Elektrotehnіka ta elektroenergetika, 2005, no.2, pp. 52-64.

3. Afanasyev K.S., Glazyrin A.S. Identifikacija skorosti asinhronnogo elektrodvigatelja laboratornogo stenda s pomoshhju filtra Kalmana i nabljudatelja Ljuenbergera [Speed calculation for laboratory induction electric motor using Kalman filter and Luenberger controller]. Electrical and control systems, 2012, no.4. pp. 66-69.

4. Ha I.-J., Lee S.-H. An online identification method for both stator and rotor resistances of induction votor without rotational transducers. IEEE Transactions on industrial electronics. 2000, vol. 47, no. 4, pp. 842–852.

5. Duran M.J., Duran J.L., Perez F., Fernandez J. Inductionmotor sensorless vector control with online parameter estimation and overcurrent protection. IEEE Transactions on industrial electronics. 2006, vol. 53, no. 1, pp. 154–161.

6. Zorkaltsev V.I. Metod naimenshih kvadratov: geometricheskie svojstva, alternativnye podhody, prilozhenija [Least square method: geometric properties, alternative approaches, applications]. Novosibirsk: VO Nauka, 1995, 220 p.

7. Louson Ch., Henson R. Chislennoe reshenie zadach metodom naimenshih kvadratov [Numerical solutions using the least square method]. Moscow: Nauka, 1986, 232 p.

8. Stephan J., Bodson M., Chiasson J. Real-time estimation of the parameters and fluxes of induction motors. IEEE Transactions on industrial electronics, 1994, vol. 30, no.3, pp. 746–759.

9. Simonik P., Hudecek P., Palacky P. Estimation of induction machine electrical parameters based on genetic algoritms. Progress in electromagnetics research symposium proceedings. Malaysia, 2012, pp. 999–1002

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