Mehmet Onur GÜLBAHÇE, Muhammed Emin KARAASLAN

Estimation of Induction Motor Equivalent Circuit Parameters from Manufacturer’s Datasheet by Particle Swarm Optimization Algorithm for Variable Frequency Drives

In recent years, industrial developments have made it necessary to control induction motors, used in both industrial and household applications, over a wide range of speeds. Thanks to vector-control algorithms, in order to control the torque in high-performance operations over wide-ranging speeds, the equivalent circuit parameters of the induction motor have to be known precisely. In this study, the equivalent circuit parameters of the induction motor are estimated only with the limited information shared by the manufacturer’s datasheets. The estimation method is based on the principle of solving nonlinear equations derived from the equivalent circuit of an induction motor by the particle swarm optimization algorithm. The proposed equation set and the algorithmic solution have been tested for 20 different induction motors and presented in comparison with the experimentally obtained equivalent circuit parameters. Moreover, the speed–torque characteristics obtained experimentally and calculated from the estimated equivalent circuit parameters for ten different selected motors are compared and the performance of the proposed algorithm is examined.

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1. A. Gezer, M. O. Gulbahce, and D. A. Kocabas, “Generalised model of multiphase Tesla’s egg of Columbus and practical analysis of 3-phase design,” Electrica, vol. 18, no. 2, pp. 151–158, 2018. [CrossRef]
2. M. G. Aydeniz, “Asenkron motorların hız algılayıcısız kontrolu¨nde yeni bir algoritmanın geli¸stirilmesi ve uygulanması,” Master of Science. Yildiz Technical Univ., Istanbul, Turkey, 2005.
3. O. Bingol, “Fuzzy logic based vector control of an induction motor with three-level inverter,” Süleyman Demirel Univ. J. Nat. Appl. Sci., pp. 452–459, 2006.
4. R. Cukur, A Comparison between Two Speed Observers and Effects of Parameter Varıatıons on the Performance of Vector Control. Institute of Science and Technology Istanbul Technical University, 2015.
5. J. Zheng, Y. Wang, X. Qin, and X. Zhang, “An offline parameter identification method of induction motor,” in 2008 7th World Congress on Intelligent Control and Automation, 2008, pp. 8898–8901. [CrossRef]
6. M. Cirrincione and M. Pucci, “Experimental verification of a technique for the real-time identification of induction motors based on the recursive least-squares,” in 7th Int. Work. Adv. Motion Control IEEE, 2002, pp. 326–334.
7. J. Stephan, M. Bodson, and J. Chiasson, “Real-time estimation of the parameters and fluxes of induction motors,” IEEE Trans. Ind. Appl., vol. 30, no. 3, pp. 746–759, 1994. [CrossRef]
8. F. Alonge, F. M. Raimondi, G. Ferrante, and F. D’Ippolito, “Parameter identification of induction motor model using genetic algorithms,” IEE Proc. Control Theor. Appl., vol. 145, no. 6, pp. 587–593, 1998. [CrossRef]
9. K. Lee, S. Frank, P. K. Sen, L. G. Polese, M. Alahmad, and C. Waters, “Estimation of induction motor equivalent circuit parameters from nameplate data,” in IEEE North Am. Power Symp. (NAPS), 2012, pp. 1–6.
10. S. C. Lima, C. A. Wengerkievicz, N. J. Batistela, N. Sadowski, P. A. da Silva, and A. Y. Beltrame, “Induction motor parameter estimation from manufacturer data using genetic algorithms and heuristic relationships,” in IEEE Braz. Power Electron. Conference (COBEP), 2017, pp. 1–6.
11. H. R. Mohammadi, and A. Akhavan, “Parameter estimation of threephase induction motor using hybrid of genetic algorithm and particle swarm optimization,” J. Eng., vol. 2014, 1–6, 2014.
12. J. Susanto and S. Islam, “Estimation of induction motor parameters using hybrid algorithms for power system dynamic studies,” in IEEE Austr. Univ. Power Eng. Conf. (AUPEC), 2013, pp. 1–6.
13. J. Pedra, “On the determination of induction motor parameters from manufacturer data for electromagnetic transient programs,” IEEE Trans. Power Syst., vol. 23, no. 4, 1709–1718, 2008. [CrossRef]
14. C. H. Ozyurt, “Parameter and speed estimation on induction motors from manufactures data and measurements,” Master of Science. Middle East Tech. Univ., Ankara, Turkey, 2005.
15. C. A. C. Wengerkievicz et al., “Estimation of three-phase induction motor equivalent circuit parameters from manufacturer catalog data,” J. Microw. Optoelectron. Electromagn. Appl., vol. 16, no. 1, pp. 90–107, 2017. [CrossRef]
16. A. I. Canakoglu, A. G. Yetgin, H. Temurtas, and M. Turan, “Induction motor parameter estimation using metaheuristic methods,” Turk. J. Electr. Eng. Comput. Sci., vol. 22, no. 5, pp. 1177–1192, 2014.
17. S. A. Al-Jufout, W. H. Al-Rousan, and C. Wang, “Optimization of induction motor equivalent circuit parameter estimation based on manufacturer’s data,” Energies, vol. 11, no. 7, p. 1792, 2018. [CrossRef]
18. M. M. Abdelaziz and E. F. El-Saadany, “Estimation of induction motor single-cage model parameters from manufacturer data,” in IEEE Power Energy Soc. Gen. Meet., 2013, pp. 1–5.
19. G. F. V. Amaral, J. M. R. Baccarini, F. C. R. Coelho, and L. M. R. Baccarini, “A High Precision method for induction machine parameters estimation from manufacturer data,” IEEE Trans. Energy Convers., vol. 36, no. 2, 1226–1233, 2020. [CrossRef]
20. M. Fan, J. Chai, and X. Sun, “Induction motor parameter identification based on T-model equivalent circuit,” in 17th Int. Conf. Electri. Mach. Syst. (ICEMS), IEEE Publications, 2014, pp. 2535–2539.
21. L. Guasch-Pesquer, L. Youb, A. A. Jaramillo-Matta, F. Gonzalez-Molina, and J. A. BarradoRodrigo, “Parameters calculation of single-and doublecage models for induction motors from manufacturer data” in Int. Conf. Optim. Electri. Electron. Equip. (OPTIM), 2015, pp. 237–242.
22. M. H. Haque, “Determination of NEMA design induction motor parameters from manufacturer data,” IEEE Trans. Energy Convers., vol. 23, no. 4, pp. 997–1004, 2008. [CrossRef]
23. J. Tang, Y. Yang, F. Blaabjerg, J. Chen, L. Diao, and Z. Liu, “Parameter identification of inverter-fed induction motors: A review,” Energies, vol. 11, no. 9, p. 2194, 2018. [CrossRef]
24. A. Ukil, R. Bloch, and A. Andenna, “Estimation of induction motor operating power factor from measured current and manufacturer data,” IEEE Trans. Energy Convers., vol. 26, no. 2, pp. 699–706, 2011. [CrossRef]
25. R. K. Ursem and P. Vadstrup, “Parameter identification of induction motors using differential evolution,” in Cong. Evol. Comput. (CEC’03). IEEE Publications, 2003, vol. 2, pp. 790–796.
26. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. ICNN'95-Int. Conf. Neural Networks, IEEE Publications, 1995, vol. 4, pp. 1942–1948.
27. S. Ozyon, C. Yasar, and H. Temurtas, “Particle swarm optimization algorithm applied to environmental economic power dispatch problems consisting of thermal units,” in 6th Int. Adv. Technol. Symp. (IATS’11), Electri. Electron. Tech. Papers, 2011, vol. 4, pp. 175–180.
28. M. Y. Ozsaglam and M. Cunkas, “Particle swarm optimization algorithm for solving optimızation problems,” J. Polytech., vol. 11, no. 4, pp. 299–305, 2008.
29. M. A. Cavuslu, C. Karakuzu, and S. Sahin, “Hardware implementation of artificial neural network training using particle swarm optimization on FPGA,” J. Polytech., vol. 13, no. 2, pp. 83–92, 2010.
30. A. J. Wood and B. F. Wollenberg, Power Generation Operation and Control. New York: Wiley, 1996.
31. S. Zorlu and F. Mergen, Electrical Machines-2 Induction Machines (in Turkish). Turkey: Birsen Publisher, 2000.
32. A. Diaz, R. Saltares, C. Rodriguez, R. F. Nunez, E. I. Ortiz-Rivera, and J. Gonzalez- Llorente, “Induction motor equivalent circuit for dynamic simulation,” in IEEE Int. Elect. Mach. Drives Conf., 2009, pp. 858–863.
33. D. W. Novotny and T. A. Lipo, Vector Control and Dynamics of AC Drives, vol. 41. Oxford: Oxford University Press, 1996.
34. M. O. Gulbahce and D. A. Kocabas, “A comprehensive approach to determining the speed/torque relationships of eddy current brakes,” Electr. Eng., vol. 100, no. 3, p. 1587, 2018. [CrossRef]