A Comparison Study of Some Metaheuristic Methods for Field Oriented Control Based Induction Motors

Öz

Thanks to the accuracy, high reliability and excellent performance, field-oriented control (FOC) based induction motor (IM) drives are used in power applications. Generally, three PI controllers are used for speed control, and decoupling control of torque and flux by d-q components of stator current in FOC based IM. Most of the time, the gains of these PI controllers are tuned by trial and error or Ziegler Nichols method. However, these methods cannot provide a high dynamic response. In this paper, some metaheuristic methods such as Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms are used to determine the optimal values of the PI controller parameters to improve the dynamic performance of FOC based IM. The simulation results show that the best dynamic performance is obtained by GWO algorithm although its convergence speed is slower than the used other algorithms.

Anahtar Kelimeler

• ABC
• Field Oriented Control
• GWO
• Induction Motor
• Metaheuristic
• PSO

Kaynakça

1. [1] I. Ferdiansyah, M.R. Rusli, B. Praharsena, H. Toar, E. Purwanto. "Speed control of three phase induction motor using indirect field oriented control based on real-time control system." 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Bali, Indonesia, 2018.
2. [2] A.Y. Yousef, S.M. Abdelmaksoud. "Review on field oriented control of induction motor." International Journal for Research in Emerging Science and Technology, vol. 2. 7, 2015, pp 5–16.
3. [3] T. Banerjee, S. Choudhuri, J. Bera, A. Maity. "Off-line optimization of PI and PID controller for a vector controlled induction motor drive using PSO." 6th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 2010.
4. [4] C. Lai, K. Peng, G. Cao. "Vector control of induction motor based on online identification and ant colony optimization." 2nd International Conference on Industrial and Information Systems, Dalian, China, 2010.
5. [5] M.B.B. Sharifian, S. Galvani, M.B. Kouhshahi. "Torque fluctuations reducing in a vector-controlled induction motor drive by PI controller tuning using particle swarm optimization.” International Conference on Electrical Machines and Systems, Beijing, China, 2011.
6. [6] M.A. Hannan, J.A. Ali, A. Mohamed, U.A.U. Amirulddin, N.M.L. Tan, M. N. Uddin. "Quantum-behaved lightning search algorithm to improve indirect field-oriented Fuzzy-PI control for IM drive." IEEE Industry Applications Society Annual Meeting, Cincinnati, OH, USA, 2017.
7. [7] F.A. Hasan, A.T. Humod, L.J. Rashad. "Robust decoupled controller of induction motor by combining PSO and Kharitonov’s theorem." Engineering Science and Technology, an International Journal, vol. 23. 6, 2020, pp 1415–1424.
8. [8] A. Raj, Y.A. Khan, V. Verma. "Comparative evaluation of PSO, TLBO, and JAYA based adaptive PI and FOPI controllers for vector controlled induction motor drive.” IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia, 2021.

9. [9] M.M. Hasan, M.S. Hussain, M.S. Rana, M.H.K. Roni. "Population extremal optimization based 2-DOF control strategy for field oriented control of induction motor." 3rd International Conference on Electrical & Electronic Engineering (ICEEE), Rajshahi, Bangladesh, 2021.
10. [10] M.J. Cheerangal, A.K. Jain, A. Das. "Control of rotor field-oriented induction motor drive during input supply voltage sag." IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9. 3, 2021, pp 2789-2796.
11. [11] R. Rai, S. Shukla, B. Singh. "Sensorless field oriented SMCC based integral sliding mode for solar PV based induction motor drive for water pumping." IEEE Transactions on Industry Applications, vol. 56. 5, 2020, pp 5056-5064.
12. [12] S. Mirjalili, S.M. Mirjalili, A. Lewis. "Grey wolf optimizer." Advances in Engineering Software, vol. 69, 2014, pp 46-61.
13. [13] M.A. Şen, M. Kalyoncu. "Optimal tuning of PID controller using grey wolf optimizer algorithm for quadruped robot." Balkan Journal of Electrical & Computer Engineering, vol. 6. 1, 2018, pp 29-35.
14. [14] D. Karaboga, B. Basturk. "On the performance of artificial bee colony (ABC) algorithm." Applied Soft Computing, vol. 8, 2008, pp 687-697.
15. [15] D. Karaboga, C. Ozturk. "A novel clustering approach: Artificial Bee Colony (ABC) algorithm." Applied Soft Computing, vol. 11, 2011, pp 652-657.
16. [16] J. Kennedy, R. Eberhart. "Particle swarm optimization." International Conference on Neural Networks, Perth, WA, Australia, 1995.
17. [17] A. Iqbal, G.K. Singh. "PSO based controlled six-phase grid connected induction generator for wind energy generation." CES Transactions on Electrical Machines and Systems, vol. 5. 1, 2021, pp 41-49.
18. [18] E. Ebrahim. "Artificial bee colony-based design of optimal on-line self- tuning PID-controller fed AC drives." International Journal of Engineering Research, vol. 3. 12, 2015, pp 807-811.
19. [19] S. R. Ahamed, J. N. Chandra Sekhar, D. P. Reddy P. "Speed control of induction motor by using intelligence techniques." International Journal of Engineering Research and Applications, vol. 5. 1, 2015, pp 130-135.
20. [20] T. G. Workineh, Y. B. Jember, A. T. Kassie. "Evaluation of intelligent PPI controller for the performance enhancement of speed control of induction motor." Scientific African, vol. 22, 2023, pp 1-16.
21. [21] P. J. Shaija, A. E. Daniel. "Optimal tuning of PI controllers for IM drive using GWO and TLBO algorithms." 5th International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023.
22. [22] B. Çavuş, M. Aktaş. "MPC-based flux weakening control for induction motor drive with DTC for electric vehicles." IEEE Transactions on Power Electronics, vol. 38. 4, 2023, pp 4430-4439.
23. [23] K. V, B. Singh. "Optimized reference points based vector control of induction motor drive for electric vehicle." IEEE Transactions on Industry Applications, vol. 59. 4, 2023, pp 4164-4174.
24. [24] A. Benhammou, H. Tedjini, Y. Guettaf, M. A. Soumeur, M. A. Hartani, O. Hafsi, A. Benabdelkader. "Exploitation of vehicle's kinetic energy in power management of tow -wheel drive electric vehicles based on ANFIS DTC-SVM comparative study." International Journal of Hydrogen Energy, vol. 46, 2021, pp 27758-2769.
25. [25] H. Sathishkumar, S. S. Parthasarathy. "A novel neuro-fuzzy controller for vector controlled induction motor drive." Energy Procedia, vol. 138, 2017, pp 698–703.
26. [26] H. Sathishkumar, S. S. Parthasarathy. " A novel neural network intelligent controller for vector controlled induction motor drive." Energy Procedia, vol. 138, 2017, pp 692–697.
27. [27] Z. Tir, O. P. Malik, A. M. Eltamaly. "Fuzzy logic based speed control of indirect field oriented controlled double star induction motors connected in parallel to a single six-phase inverter supply." Electric Power Systems Research, vol. 134, 2016, pp 126-133.
28. [28] S. Pravallika, J. N. Chandra sekhar, D. P. Reddy P. "Optimization of Speed Control of Induction Motor Using Self Tuned PI plus Fuzzy Hybrid Controller." International Journal of Emerging Technology and Advanced Engineering, vol. 5. 1, 2015, pp 258-262.
29. [29] S. P J, A. E. Daniel. "Robust sliding mode control strategy applied to IFOC induction motor drive." 4th International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2021.
30. [30] A. Zaafouri, C. B. Regaya, H. B. Azza, A. Châari. "DSP-based adaptive backstepping using the tracking errors for high-performance sensorless speed control of induction motor drive." ISA Transactions, vol. 60, 2016, pp 333-347.