Control Neuro-Fuzzy of a Dual Star Induction Machine (DSIM) supplied by Five-Level Inverter
Abstract
This work relates to a hybrid scheme (Neuro-Fuzzy) for speed control of a dual star induction motor (DSIM) with enhancedperformance. A 4-layer network is utilized to set the Fuzzy elements in order to minimize error square. To control this machine,two five-level inverters with PWM techniques are introduced and an indirect field oriented method is used. Simulation resultsare presented for the NF controller; it is observed that the NFC gives better responses and robustness for the speed controlof this machine with its load disturbances and parameter variations, such as increased rotor resistance and moment ofinertia. The results are compared with results obtained from a conventional inverter. Notably, there is a great drop in thestator currents, and the magnitude of the pulsating electromagnetic torque is reduced for a five-level inverter compared witha conventional inverter.References
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REVUE ROUMAINE DES SCIENCES TECHNIQUES-SERIE
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3 (6) (2011) 789.
[26] C. Elmas, O. Ustun, H. H. Sayan, A neuro-fuzzy controller for speed
control of a permanent magnet synchronous motor drive, Expert Systems
with Applications 34 (1) (2008) 657–664.
[27] F. Amrane, A. Chaiba, S. Mekhilef, High performances of gridconnected
dfig based on direct power control with fixed switching frequency
via mppt strategy using mrac and neuro-fuzzy control, Journal
of Power Technologies 96 (1) (2016) 27.
Electric Power Systems Research 61 (2) (2002) 139–147.
[2] M. Jones, E. Levi, A literature survey of state-of-the-art in multiphase
ac drives, in: Proc. 37th Int. Universities Power Eng. Conf. UPEC,
2002, pp. 505–510.
[3] R. Bojoi, F. Farina, F. Profumo, A. Tenconi, Dual-three phase induction
machine drives control—a survey, IEEJ Transactions on Industry
Applications 126 (4) (2006) 420–429.
[4] M. Lazzari, P. Ferraris, Phase number and their related effects on the
characteristics of inverter-fed induction motor drives, in: Conf. Rec. of
IEEE Industry Applications Annual Meeting, IAS’83, Vol. 1, 1983, pp.
494–502.
[5] E. Levi, Recent developments in high performance variable-speed multiphase
induction motor drives, in: sixth international symposium nikola
tesla, 2006.
[6] S. Y. Yi, M. J. Chung, Robustness of fuzzy logic control for an uncertain
dynamic system, IEEE Transactions on Fuzzy Systems 6 (2) (1998)
216–225.
[7] M. N. Uddin, T. S. Radwan, M. A. Rahman, Performances of fuzzylogic-
based indirect vector control for induction motor drive, IEEE
Transactions on Industry Applications 38 (5) (2002) 1219–1225.
[8] R. Krishnan, F. C. Doran, Study of parameter sensitivity in highperformance
inverter-fed induction motor drive systems, IEEE Transactions
on Industry Applications (4) (1987) 623–635.
[9] R. Sadouni, A. Meroufel, Indirect rotor field-oriented control (irfoc) of a
dual star induction machine (dsim) using a fuzzy controller, Acta Polytechnica
Hungarica 9 (4) (2012) 177–192.
[10] S. Lekhchine, T. Bahi, Y. Soufi, Indirect rotor field oriented control
based on fuzzy logic controlled double star induction machine, International
Journal of Electrical Power & Energy Systems 57 (2014) 206–
211.
[11] E. Merabet, H. Amimeur, F. Hamoudi, R. Abdessemed, Self–tuning
fuzzy logic controller for a dual star induction machine, Journal of Electrical
Engineering & Technology 6 (1) (2011) 133–138.
[12] M. Bouziane, M. Abdelkader, A neural network based speed control
of a dual star induction motor, International Journal of Electrical and
Computer Engineering 4 (6) (2014) 952.
[13] M. T. Wishart, R. G. Harley, Identification and control of induction machines
using artificial neural networks, IEEE Transactions on Industry
Applications 31 (3) (1995) 612–619.
[14] S. Lekhchine, T. Bahi, Y. Soufi, H. Merabet, Neural fuzzy speed control
for six phase induction machines, Proceedings Engineering & Technology
(PET) 1 (2013) 12–26.
[15] M. Nasir Uddin, M. Abido, M. Rahman, Development and implementation
of a hybrid intelligent controller for interior permanent magnet
synchronous motor drives, IEEE Transactions on Industry Applications
40 (1) (2004) 68–76.
[16] J.-S. Jang, Anfis: adaptive-network-based fuzzy inference system,
IEEE transactions on systems, man, and cybernetics 23 (3) (1993)
665–685.
[17] J.-S. Jang, Self-learning fuzzy controllers based on temporal backpropagation,
IEEE Transactions on neural networks 3 (5) (1992) 714–
723.
[18] J.-S. Jang, C.-T. Sun, Neuro-fuzzy modeling and control, Proceedings
of the IEEE 83 (3) (1995) 378–406.
[19] B. S. Marwa, K. M. Larbi, B. F. Mouldi, R. Habib, Modeling and analysis
of double stator induction machine supplied by a multi level inverter,
in: Electrotechnical Conference (MELECON), 2012 16th IEEE
Mediterranean, IEEE, 2012, pp. 269–272.
[20] K. Iffouzar, S. Taraft, H. Aouzellag, K. Ghedamsi, D. Aouzellag, Behavior
of a six phase induction motor fed by multilevel inverter, in: Electrical
Engineering (ICEE), 2015 4th International Conference on, IEEE,
2015, pp. 1–7.
[21] K. Iffouzar, M.-F. Benkhoris, K. Ghedamsi, D. Aouzellag, Behavior
analysis of a dual stars induction motor supplied by pwm multilevel inverters,
REVUE ROUMAINE DES SCIENCES TECHNIQUES-SERIE
ELECTROTECHNIQUE ET ENERGETIQUE 61 (2) (2016) 137–141.
[22] I. Colak, E. Kabalci, R. Bayindir, Review of multilevel voltage source
inverter topologies and control schemes, Energy conversion and management
52 (2) (2011) 1114–1128.
[23] A. Berboucha, K. Djermouni, K. Ghedamsi, D. Aouzellag, Utilisation a
fuzzy controller optimized by genetic algorithm in photovoltaic pumping
system, in: 7th International Conference on Electrical Engineering
EEC in Batna, 2012.
[24] K. Ghedamsi, Design and realization of different strategies pwm control
of the three-phase three-level inverter, Magister Memory Ecole Militaire
Polytechnique.
[25] P. Thongprasri, A 5-level three-phase cascaded hybrid multilevel inverter,
International Journal of Computer and Electrical Engineering
3 (6) (2011) 789.
[26] C. Elmas, O. Ustun, H. H. Sayan, A neuro-fuzzy controller for speed
control of a permanent magnet synchronous motor drive, Expert Systems
with Applications 34 (1) (2008) 657–664.
[27] F. Amrane, A. Chaiba, S. Mekhilef, High performances of gridconnected
dfig based on direct power control with fixed switching frequency
via mppt strategy using mrac and neuro-fuzzy control, Journal
of Power Technologies 96 (1) (2016) 27.
Published
2018-04-06
How to Cite
LARAFI, BENTOUHAMI et al.
Control Neuro-Fuzzy of a Dual Star Induction Machine (DSIM) supplied by Five-Level Inverter.
Journal of Power Technologies, [S.l.], v. 98, n. 1, p. 70-79, apr. 2018.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1031>. Date accessed: 25 apr. 2024.
Issue
Section
Electrical Engineering
Keywords
Dual stars induction machine (DSIM), Indirect field oriented control (IFOC), five-level inverter, Neuro-fuzzy cntroller (NFC).
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