ARTIFICAL INTELLIGENCE BASED METHODS FOR SENSORLESS VECTOR CONTROL OF INDUCTION MOTOR

  • G.SRINIVAS Assistant professor (gitam deemed to be university) Hyderabad.

Abstract

Three phase induction motors are the most sought after machines in industry as they are relatively less costly and rugged due to the absence of commutator. They are the driving mechanism for majority of operations in industries, agriculture, commercial complexes etc. but in the separately excited DC machines, because of the presence of commutator, the flux axis and the armature axis are always in quadrature. Hence, there is always inherent decoupling between main flux and the armature flux called vector or decoupled control which leads to flexible operation and hence accurate control. Since, induction motor is singly fed, stator current has to meet with both torque and flux requirements. Hence, it is not possible to control both components independently and this is the main cause for the sluggish behavior of induction motors. Induction motor performance can be made similar to that of DC machine by resolving the stator current into flux producing component and torque producing component of current. The difficulty here is the determination of flux axis so that the flux component of stator current can be along that axis. In order to carry out this, the information regarding the exact position of rotor position as well as its magnitude is required. Depending on how the information is collected, vector control is divided in to two classes, namely, direct and indirect field oriented control schemes. In the direct method the use of hall probes or search coils used for flux measurement, destroy the ruggedness of the motor. Instead, rotor position can be estimated using machine models where indirect method or sensor less control aims at using mathematical expressions.


The basic foundation needed for vector control of induction motor


(IM) is decoupling of stator currents into flux and torque components along the rotor flux axis. For this information the instantaneous rotor position is necessary. Depending on the methods employed for finding rotor position vector control are two types. Direct vector control (DVC) and indirect vector control (IVC). In direct vector control the rotor position is sensed by Hall Effect sensors introduced in the stator. The basic drawback is it introduces harmonics in the output voltage and results in addition of cost and size. In Sensorless vector control (SVC) the rotor position is estimated by using mathematical analysis and machine dynamic model which eliminates speed sensors, encoder and motor shaft extension and hence reduces cost and ruggedness.


The basic methods employed for detection of rotor position by sensor less control involve: Conventional methods like Kalman filter method, Conventional PI Controller, Artificial intelligence methods like Fuzzy and ANN and Evolutionary methods like Genetic algorithms and Particle Swarm Optimization.


In conventional methods like PI Control method and Extended Kalman filter method of estimation is done by using motor equations to directly compute speed and are prone to numerical and steady state errors. Hence a new method is suggested which employs latest simulating and computing techniques like Artificial intelligence methods like Fuzzy controller and ANN and Evolutionary control methods like GA and PSO are used for multivariable state feedback linearization method whose load torque is estimated by the above suggested artificial and evolutionary methods, hence the errors in the above conventional methods can be minimized.


This research aims at designing a controller using conventional methods like PI controller ,Extended Kalman filter and artificial intelligent methods like fuzzy ,neural networks and evolutionary methods like genetic algorithms and particle swam optimization and to find speed and torque responses using parameters like peak overshoot ,peak time rise time etc. and to reduce steady state errors in conventional methods and to implement hardware using FPGA Module for conventional methods like Kalman filter and Conventional PI Controller and compare with Proposed methods like GA and PSO.

Keywords: majority of operations in industries, agriculture, commercial complex

References

1. Pillay, P, V. Levin, "Mathematical models for induction machines", IEEE paper, 1995, pp. 606-617.
2. G. 0. Garcia, J. C. Mendes Luis, R.M. Stephan, and E. H. Watanabe "An Efficient Controller for an Adjustable Speed IM Drive", IEEE transaction on industrial electronics, Vol. 41, Issue No. 5, October 1994, pp. 533-39.
3. Krishnan R “Electric Motor Drives-Modeling, Analysis, and control, Pearson Education, Inc.”, Delhi, India, 2003,pp. 128 – 215.
4. B. K. Bose, “Modern Power Electronics and AC Drives”, Prentice Hall PTR, Upper Saddle River, 2002.
5. Adrian Dumitrescu, Denes Fodor, TapaniJokinen, Marius Rosu, Sorin. "Modeling And Simulation of Electric Drive Systems Using Mat lab/ Simulink Environments", IEEE Int. Conf. on Electric Machines and Drives IEMD-99, Seattle, USA, May 1999,pp. 451-453.
6. Hoang Le-Huy "Modeling and Simulation of Electrical Drives using MATLAB/Simulink and Power System Block set", IEcon'01: The 27th Annual Conference of the IEEE Industrial Electronics Society, 2001, pp.1603-1611.
7. Peter vas "Sensorless vector control and direct torque control" Monographs in electrical and electronics engineering-42, Oxford science in 1998.
8. Joachim Holtz "Sensorless control of induction motor drives", IEEE,June 2017, Vol. 90, No. 8,August 2002, pp.1359-1394.
9. Poddar, VT Ranganathan "Sensorless double-inverter-fed wound-rotor induction-Machine drive", Industrial Electronics, IEEE transactions on 53 (1), pp. 86-95.
10. Epaminondas D. Mitronikas and Athanasios N. Safacas, Member, IEEE "An Improved Sensorless Vector-Control Method for an IM Drive", IEEE transactions on industrial electronics, December 2005, Vol. 52, Issue No. 6, pp. 1148-57.
11. SB Bodkhe, MV Aware "Speed-Sensorless, adjustable-speed induction motor drive based on dc link measurement",International Journal of Physical Sciences,April 2009, Vol. 4, Issue No. 4, pp. 221-232.
12. Mustafa Gurkan Aydeniz, Ibrahim Senol "A novel approach to sensorless control of induction motors", International Conference on Electrical and Electronics Engineering - ELECO 2009 Publication Year: N0V- 2009, pp. I-179 -I-183.5-8 5-8.
13. A Ansari, 2 DM Deshpande "Mathematical Model of Asynchronous Machine in MATLAB Simulink", International Journal of Engineering Science and Technology,2010, Vol. No. 2(5), pp. 1260-1267.
14. Jogendra Singh Thongam and Rachid Beguenane "Sensorless Vector Control of Induction Motor Drive -A Model-Based Approach",February 2011, pp. 77-96.
15. Osama S. Ebrahim and Praveen K. Jain, Fellow IEEE "NEW SVC Scheme for the IM Drive",International Conference on Electrical and Electronics Engineering, ELECO2009,pp.179 - 183.
16. Américo Vicente Leite, Rui Esteves Araújo, and Diamantino Freitas “A New Approach for Speed Estimation in Induction Motor Drives Based on a Reduced-Order Extended Kalman Filter”, 0-7803-8305-2, 2004, IEEE, pp. 1221-1226.
17. Kanungo Barada Mohanty and Amit Patra, "Flux and speed estimation in decoupled induction motor drive using Kalman Filter", Proceedings of national system conference, IIT Mumbai, Dec 2005, pp. 1-9.
18. R.Gunabalan, V.Subbiah and B.Ram Reddy, "Sensorless Control of Induction Motor with Extended Kalman Filter on TMS320F2812 Processor", International Journal of Recent Trends in Engineering, November 2009, Vol. 2, Issue No. 5, pp.14-19.
19. Pavel Brand Stetter, Martin Kuchar, David Vinklarek "Estimation Techniques for Sensorless Speed Control of IM Drive", IEEE ISIE,Montreal, Quebec, Canada, July 9-12, 2006, pp. 154-159.
20. Nadia Salvatore, Member, IEEE, Andrea Capone, Student Member, IEEE, FerranteNeri, Member, IEEE, Silvio Stasi, and Giuseppe Leonardo Casella, Member, IEEE "Optimization of Delayed-State Kalman-Filter- Based Algorithm via Differential Evolution for Sensorless Control of IMs", IEEE transactions on industrial electronics, Jan 2010Vol. 57, Issue No. 1, pp. 38.
21. Abdel Merabet, Aman A., Karim Beddak. "Torque and State estimation for real-time implementation of multivariable control in sensorless induction motor drives", IET Electric Power Appl, Vol. 11, Issue No. 4,pp. 653-663.
22. Gilberto C.D. Sousa, Bimal Bose, John Cleland "Fuzzy logic based on line efficiency optimization of an indirect vector controlled induction motor drive", IEEE transactions on industrial electronics,April 1995, Vol. 42, Issue No. 2, pp. 192-198.
23. MW. Turner, V.E. McCormick, and J.G. Cleland "Efficiency Optimization Control of AC Induction Motors", Initial Laboratory Results Research and Development EPA/600/SR-96/008 May 1996, pp. 1-8.
24. John G. Cleland, Vance E. McCormick and M. Wayne Turner "A Fuzzy-Logic-Based Energy Optimizer for AC Motors", Centre for Digital Systems Engineering, Research Triangle Institute, Research Triangle Park, NC 27709, pp. 1777-1784.
25. Mao-Fu Lai, Chen Chang, and Wen-YuhChiou, "Design of fuzzy logic controllers for an IM speed drive", SICE-1997 Int. Conf., Tokushima, Jul. 29-31, 1995, pp. 1071-1076.
26. Juan M. Moreno-Eguilaz, M. Cipolla-Ficarra, P.J. DaCosta Branco, Juan Peracaula, "Fuzzy logic improvements in efficiency optimization of induction drives", Proceedings of 6th International Fuzzy Systems Conference, July 1997, Vol. 3, pp. 1-5.
27. Mokrani, R. Abdessemed, "A Fuzzy Self-Tuning PI-controller for Speed Control of IM Drive",Proc. IEEE Int. Conf. 2003, pp. 785-790.
28. V. Chitra, and R. S. Prabhakar "IM Speed Control using Fuzzy Logic Controller", World Academy of Science, Engineering and Technology 23, 2006.
29. Abdelkader Chaari, MoezSoltani "Comparative study between the conventional regulators and fuzzy logic controller: Application on the induction machine", International Journal of Sciences and Techniques of Automatic control & computer engineering IJ-STA, Vol.1, Issue No. 2, December 2007, pp. 196?212.
30. Satean Tunyasrirut, TianchaiSuksri, and SompongSrilad "Fuzzy Logic Control for a Speed Control of IM using Space Vector Pulse Width Modulation", World Academy of Science, Engineering and Technology 25 Aug 2007, pp. 7-13.
31. RajuYanamshetti, Sachin.Bharathiar, DebashisChatterje, K.Ganguli "A Hybrid Fuzzy Based Loss Minimization Technique for fast Efficiency Optimization for a variable speed induction machine",Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009. I-CON 2009, 6th International Conference, May 2009, pp. 318-321.
32. ArunimaDey, Bhim Singh, Bharti Dwivedi and Dinesh Chandra "Vector control of three-phase induction motor using the artificial intelligent technique", ARPN Journal of Engineering and Applied Sciences Vol. 4, Issue No. 4, JUNE 2009, ISSN: 1819-6608, pp. 57-67.
33. K.Ranjitkumar, D.Saktibala, Dr.S.Palaniswami "Efficiency optimization of induction motor using soft computing techniques", International journal of computer applications, June 2010, Vol. 3, Issue No. 1, pp. 6-12.
34. Kung, Y.S., C.M. Liaw, MS Ouyang, "Adaptive Speed Control for IM Drives Using N-Ns",IEEE Trans. On Ind. Electronics, Feb. 1995, Vol. 42, Issue No. 1, pp. 25-32.
35. Keerthipala, W.W.L., BR Dug gal, and Miao Hua Chun, "Torque and speed control of IM using ANN observers",IEEE Int. Conf. on Power Electronic Drives and Energy Systems for Industrial Growth, Vol. 1, 1-3 Dec. 1998, pp. 282-288.
36. Seong-Hwan Kim, KTae-Sik Park, Ji-Yoon Yoo im "Speed-Sensorless Vector Control of an Induction Motor Using Neural Network Speed Estimation",IEEE trans. On Ind. Electronics, June 2001, Vol. 48, Issue No. 3, pp. 609-614.
37. Bedri OZER Erhan AKIN "ANN-based speed estimator for vector controlled IM" Firat University, Department of Computer Engineering, 2001, pp.281-286.
38. Won Seok Oh, Bose, B.K., Kyu Min Cho, Hee-Jun Kim, "Self-tuning N-N controller for IM drives",IEEE IECON-2002 28th Annual Conference of the Industrial Electronics Society, Vol. 1, 2002, pp. 152-156.
39. F. Haghgoeian, M.A.Ouhrouche, S. Thong am"Speed Estimation Using N-N in Vector Controlled IM Drive", 2005 WSEAS Int. Conf. on dynamical systems and CONTROL, Venice, Italy, November 2-4, 2005, pp.592-597.
40. A.K. Sharma, R. A. Gupta, LaxmiSrivastava "Performance of ANN-based indirect vector controlled induction motor drive",Journal of Theoretical and Applied Information Technology, September 2007,pp.50-57.
41. Syed Abdul RahmanKashif and Muhammad Asghar Saqib, "Soft Starting of IMs using Neuro-Fuzzy and Soft Computing", Second IEEE Int. Conf. on Electrical Engg., Univ. of Engg. AnandTech.ICEE- 2008, Lahore, Pakistan, 25-26 March 2008, pp. 1-7.
42. Hongjie and Li Dedi, "ANN Based Model Reference Speed Control for High Precision Motion Control Systems",Tenth Int. Conf. on Computer Modeling and Simulation, UKSIM'0, April 2008, pp. 236-240.
43. Shady M. Gadoue, Damian Giaouris, John W. Finch "Sensorless Control of Induction Motor Drives at Very Low and Zero Speed Using Neural Network Flux Observers", IEEE transactions on industrial electronics, august, 2009, Vol. 56, Issue No. 8, pp.3019-3039.
44. Wassim. A Bedwani, and Osama M. Ismail, "Genetic Optimization of Variable Structure PID Control Systems", Computer Systems and Applications," ACS/IEEE International Conference in 2001, pp. 27-30.
45. Andrew Trentin, Pericle Zanchetta, Patrick Wheeler, Jon Clare Robert Wood, DimosKatsis "A New Method for IMs Parameter Estimation Using GAs and Transient Speed measurements",1-4244-0365-0, 2006 IEEE, pp. 2435-2440.
46. Francesco Cupertino, Ernesto Mininno, Erika Lino "Optimization of Position Control of Induction Motors using Compact Genetic Algorithms", Technical University of Bari, Orabona st., 4 70125 Bari, ITALY IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics 32 Annual Conference, 2006, pp.55-60.
47. Mahomet Çunkasa, and RamazanAkkaya, "Design optimization of induction motor by genetic algorithm and comparison with existing motor", Department of Electronics and Computer Education, Selçuk University, Konya, 42075, TURKEY Department of Electrical and Electronics Engineering, Selçuk University, Konya, 42031, TURKEY page 193-203. Mathematical and Computational Applications, Vol. 11, Issue No. 3, 2006, pp. 193-203.
48. Z. Rouabah, F. Zidani, B. Abdelhadi "Efficiency Optimization of IM Drive using Fuzzy Logic and GAs", 978-1-4244-1666-0, 2008 IEEE, pp. 737-742.
49. Subramanian, R., Sivanandam, S.N. and Vimalarani, C. "An optimization of design for S4-duty IM using constraints normalization based violation technique", Journal of Computer Science, Vol. 6, Issue No. 2, 2010, pp. 107 -111.
50. Mohamed Chebre1, Abd-el-Kader Meroufel2, Bessemer Bendaha "Speed Control of IM Using GA-based PI-controller", Department of Electrical Engineering, University of Sciences and Technology (USPTO) BP 1505 EL M" naouer, Oran Algeria, Vol. 8, Issue No. 6, 2011, pp. 141-153.
51. W.S.Oh, K.M.Cho, S.Kim, and H.J.Kim "Optimized N-N Speed Control of IM using GA", International Symposium on Power Electronics, Electrical Drives, Automation and Motion Speedam 2006, pp. 1377-1380.
52. “Russell Eberhart New Optimizer Using Particle Swarm Theory", Purdue School of Engineering and Technology Indianapolis, IN 46202-5 160, 2012, pp. 39-43.
53. Y. Del Valle, G. K. Venayagamoorthy, S. Mohagheghi, J. C. Hernandez, R. G. Harley, "Particle swarm optimization: Basic concepts, variants, and applications in power systems", IEEE Transactions on Evolutionary Computation 12 (2), 2008,pp.171-195.
54. SapnaKatiyar “Comparative Study of GA and the Particle Swarm Optimization", ABES Institute of Technology, NH-24, Vijay Nagar, Ghaziabad 2010UPin,akgec International Journal Of Technology, Vol. 2, Issue No. 2, pp. 21-24.
55. Sajedi, F. Khalifeh, Z. Khalifeh and T. Karimi "Application of Particle Swarm Optimization and GA methods for Vector Control of IMS", Department of Electrical Engineering, Kavar Branch, Islamic Azad University, Kavar, Iran. Australian Journal of Basic and Applied Sciences, 5(12), 2011 ISSN 1991-8178, pp. 1697-1706.
56. K. Tazi, E. Monmasson, "Single-Chip DSP Based Speed Control of Two AC-Machine", Speedam, Sorrento (Italy), 1998, pp. 33 -38.
57. T. Takahashi, J. Goetz "Implementation of Complete AC Servo Control in a Low-Cost FPGA and Subsequent ASSP Conversion", IEEE, 2004. Engineering 1 (2), Me dwell Journals, 2007, pp. 225-259,
58. GG Parma, V. Dinavahi, "Real-Time Digital Hardware Simulation of Power Electronics and Drives", IEEE transactions on power delivery,2007, Vol. 22, Issue No. 2,pp.1235-1246.
59. M.Cristea, A.Aounis, M.Macromick, P.Urwin, L.Haydock, "Induction Motor Drive System Modelled in VHDL", Proceedings VHDL International Users Forum Fall Workshop IEEE August 2002, ISBN 0-7695-0890-1, pp. 118-122.
60. B. Hariram, N. S. Marimuthu, "A VHDL Library of Modules for Vector Control of IM", International Journal of Electrical and Power, pp. 255-259.
61. G. Mailloux, S. Simard, R. Beguenane, "Implementation of Division and Square Root Using XSG for FPGA Based Vector Control Drives", International Journal of Electrical and Power Engineering 1 (5), pp. 524-529.
62. S. K. Shoo, G. T. R. Das, V. Subramanian "Implementation and Simulation of Direct Torque Control Scheme with the Use of FPGA Circuit", ARPN Journal of Engineering and Applied Sciences, Vol. 3, Issue No. 2, ISSN: 1819-6608, 2008, pp. 48-54.
63. Ozkan AKIN, Irfanalan "The use of FPGA in field-oriented control of an induction machine", Turk J Elec Eng& Comp Sci, Vol.18, Issue No.6, 2010, pp. 943-962.
64. J. Vásárhelyi, M. Imecs, CS. Szabó, I. Incze, T. Adám“FPGA Implementation of the Reconfigurable Control System for AC Drives Fed by Tandem Converter”, Romania, CD-ROM, Vol. 2, 2011, pp.475-448.
65. R.Rajendran, Senior Member IACSIT and Dr.N.Devarajan"FPGA Based Implementation of Space Vector Modulated Direct Torque Control For IM Drive", International Journal of Computer and Electrical Engineering, June 2010,Vol. 2, No. 3, pp.1793-8163.
Statistics
4 Views | 4 Downloads
How to Cite
G.SRINIVAS. (2021). ARTIFICAL INTELLIGENCE BASED METHODS FOR SENSORLESS VECTOR CONTROL OF INDUCTION MOTOR. Innovare Journal of Engineering and Technology, 9(1), 1-17. Retrieved from https://innovareacademics.in/journals/index.php/ijet/article/view/42147
Section
Original Article(s)