Virtual lab for artificial intelligence controllers based speed control for induction motor

Year 2018, , 29 - 36, 31.12.2018

Okan Bingöl , Serdar Paçacı

https://doi.org/10.31590/ejosat.443601

Abstract

Practical implementation has an important role
in engineering education. Practical implementations, however, may not be
possible in some situations, such as lack of physical possibilities, the
presence of situations that may create risks during implementation, or
place-time dependence. So, package programs are developed for the virtual
practical implementation experience. On the other hand, these tools may not be
flexible and interactive enough for all branches of science. Therefore, in this
study a virtual laboratory tool was developed for the speed control of an
induction motor fed by a three-level inverter. The user can select
proportional-integral, proportional–integral–derivative, fuzzy logic,
artificial neural network, and neuro-fuzzy controllers for the speed
controller. Different working conditions for the induction motor can be
simulated and the outcomes can be observed by the users. The virtual laboratory
had a flexible interface and it was written on Microsoft Visual Studio 2015 IDE
using C# programming language on Windows Presentation Foundation
infrastructure.

Keywords

Virtual laboratory, inverter, neuro-fuzzy control, space vector modulation

Virtual lab for artificial intelligence controllers based speed control for induction motor

Abstract



Keywords

Virtual laboratory, inverter, neuro-fuzzy control, space vector modulation

References

• Deperlioğlu, Ö., Köse, U. 2011. An educational tool for artificial neural networks. Computers & Electrical Engineering, 37(3): 392–402, 2011. DOI:10.1016/j.compeleceng.2011.03.010
• Sevgi, L. 2006. Modeling and simulation concepts in engineering education: virtual tools. Turkish Journal of Electrical Engineering & Computer Sciences, 14(1): 113–127, 2006.
• Öztürk, N., Çelik, E. 2014. An educational tool for the genetic algorithm-based fuzzy logic controller of a permanent magnet synchronous motor drive. International Journal of Electrical Engineering Education, 51(3): 218–231, 2014. DOI:10.7227/ijeee.51.3.4
• Potkonjak V., Gardner, M., Callaghan, V., Mattila, P., Guetl, C., Petrović, VM., Jovanović, K. 2016. Virtual laboratories for education in science, technology, and engineering: A review. Computers & Education, 95: 309–327, 2016. DOI:10.1016/j.compedu.2016.02.002
• Keyhani, MN., Marwali, LE., Higuera, G., Athalye, G., Baum-gartner. 2002. An integrated virtual learning system for the development of motor drive systems. IEEE Trans.on Power Systems, 17(1): 1–6, 2002. DOI:10.1109/59.982185
• Köse, U., Deperlioğlu, Ö. 2015. FL-LAB v2: Design and Development of an Easy-to-Use, Interactive Fuzzy Logic Control Software System. Applied Mathematics & Information Sciences, 9(2): 883–897, 2015. DOI:10.12785/amis/090237
• Avouris, NM., Tselios, N., Tatakis, EC. 2001. Development and evaluation of a computer-based laboratory teaching tool. Computer Applications in Engineering Education, 9(1): 8–19, 2001. DOI:10.1002/cae.1001
• Kayıslı, K., Tuncer, S., Poyraz, M. 2013. An educational tool for fundamental DC–DC converter circuits and active power factor correction applications. Computer Applications in Engineering Education, 21(1): 113-134, 2013. DOI:10.1002/cae.20455
• Yigit, T., Elmas, Ç. 2008. An educational tool for controlling SRM. Computer Applications in Engineering Education, 16(4): 268–279, 2008. DOI:10.1002/cae.20148
• Koku, AB., Kaynak, O. 2001. An internet-assisted experimental environment suitable for the reinforcement on undergraduate teaching of advanced control techniques, IEEE Trans. on Education, 44(1): 24–28, 2001. DOI: 10.1109/13.912706
• Altas, IH., Aydar, H. 2008. A real time computer controlled simulator for control systems. Computer Applications in Engineering Education, 16(2): 115–126, 2008. DOI:10.1002/cae.20130
• Gökbulut, M., Bal, C., Dandıl, B. 2006. A virtual electrical drive control laboratory: neuro–fuzzy control of induction motors. Computer Applications in Engineering Education, 14(3): 211–221, 2006. DOI:10.1002/cae.20130
• Bingöl, O., Paçacı, S. 2012. A virtual laboratory for neural network controlled DC motors based on a DC-DC buck converter. The International Journal of Engineering Education, 28(3): 713–723, 2012.
• Bingöl, O., Paçacı, S. 2010. A virtual laboratory for fuzzy logic controlled DC motors. International Journal of Physical Sciences, 5(16): 2493–2502, 2010.
• Sobczuk, DL. 2007. Internet based teaching of pulse width modulation for three-level converters. EUROCON The International Conference on “Computer as a Tool” Warsaw, September 9-12: 2479–2484, 2007. DOI:10.1109/eurcon.2007.4400592
• Boldea İ., Nasar, SA. 1992. Vector control of AC drives. CRC Press, New York.
• Wai RJ., Chang, HH. 2004. Backstepping wavelet neural network control for indirect field-oriented induction motor drive. IEEE Trans. on Neural Networks, 15(2): 367–382, 2004. DOI:10.1109/tnn.2004.824411
• Akçayol, MA., Çetin, A., Elmas, Ç. 2002. An educational tool for fuzzy logic-controlled BDCM. IEEE Trans. on Education, 45(1): 33–42, 2002. DOI:10.1109/13.983219
• Kaiser, MS., Chowdhury, ZI., Al Mamun, S., Hussain, A., Mahmud, M. 2016. A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Cognitive Computation, 8(5), 946-954, 2016. DOI:10.1007/s12559-016-9398-4
• Sun, J., Li, Z. 2015. Development and Implementation of a wheeled inverted pendulum vehicle using adaptive neural control with extreme learning machines. Cognitive Computation, 7(6), 740-752, 2015. DOI:10.1007/s12559-015-9363-7
• Öztürk, N., Çelik, E. 2012. Speed control of permanent magnet synchronous motors using fuzzy controller based on genetic algorithms. International Journal of Electrical Power & Energy Systems, 43(1): 889–898, 2012. DOI:10.1016/j.ijepes.2012.06.013
• Weerasooriya, S., El-Sharkawi, M. 1991. Identification and control of a dc motor using back-propagation neural networks. IEEE Trans. Energy Conversion, 6(4): 663–669, 1991. DOI:10.1109/60.103639
• Narendra, KS., Parthasarathy, K. 1990. Identification and control of dynamical systems using neural networks. IEEE Trans. on Neural Networks, 1(1): 1–27, 1990. DOI:10.1109/72.80202
• Lee YH., Suh, BS., Hyun, DS. 1994. A novel PWM scheme for a three-level voltage source inverter with GTO thyristors. IEEE Trans. on Industry Applications, 32(2): 260–268, 1994. DOI:10.1109/ias.1994.377573
• Lai, JS., Peng, FZ. 1996. Multilevel converters-A new breed of power converters. IEEE Trans. on Industry Applications, 32(3): 509–517, 1996. DOI:10.1109/ias.1995.530601
• Nabae, A., Takahashi, I., Akagi, H. 1981. A new neutral-point-clamped PWM inverter. IEEE Trans. on Industry Applications, IA-17: 518–523, 1981. DOI:10.1109/tia.1981.4503992
• Rodriguez, J., Lai, JS., Peng, FZ. 2002. Multilevel inverters: A survey of topologies, controls, and applications. IEEE Trans. on Industrial Electronics, 49(4): 724–738, 2002. DOI:10.1109/tie.2002.801052
• Lin, BR., Lu, HH. 1999. Multilevel AC/DC/AC converter for AC drives. Electric Power Applications, IEE Proceedings. 146(4): 397–406, 1999. DOI:10.1049/ip-epa:19990253
• VanDer Broeck, HW., Skudelny, HC., Stanke, GV. 1988. Analysis and realization of a pulse width modulator based on voltage space vectors. IEEE Trans. on Industry Applications, 24(1): 142–150, 1988. DOI:10.1109/28.87265
• Celanovic, N., Boroyevich, D. 2000. A comprehensive study of neural-point voltage balancing problem in three-level neutral-point-clamped voltage source PWM inverters. IEEE Trans. on Power Electronics, 15(2): 242–249, 2000. DOI:10.1109/apec.1999.749733
• Yamanaka, K., Hava, AM., Kirino, H., Tanaka, Y., Koga, N., Kume, T. 2002. A novel neutral point potentail stabilization technique using the information of output current polarities and voltage vector. IEEE Trans. on Industry Applications, 38(6): 1572–1580, 2002. DOI:10.1109/ias.2001.955552
• Zadeh, LA. 1965. Fuzzy sets, in Information and Control. New York: Academic, 8: 338–353, 1965. DOI:10.1016/s0019-9958(65)90241-x
• Elmas, Ç., Sağıroğlu, Ş., Çolak, İ., Bal, G. 1994. Nonlinear modelling of a switched reluctance drive based on neural networks. IEEE Melecon 94, 7th Mediterranean Electrotechnical Conference, 2: 809–812, 1994. DOI:10.1109/melcon.1994.380979
• Elmas, Ç. 2011. Artificial Intelligence Applications: Artificial Neural Networks, Fuzzy Logic and Genetic Algorithm, Seçkin Press, Ankara, 1–424, 2011.
• Elmas, Ç., Üstün, O., Sayan, HH. 2008. A neuro-fuzzy controller for speed control of a permanent magnet synchronous motor drive. Expert Systems with Applications, 34(1): 657–664, 2008. DOI:10.1016/j.eswa.2006.10.002
• Elmas, Ç., Üstün, O. 2008. A hybrid controller for the speed control of a permanent magnet synchronous motor drive. Control Engineering Practice, 16(3): 260–270, 2008. DOI:10.1016/j.conengprac.2007.04.016