PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Rule-based Fuzzy and V/f Control for Induction Motor Speed Responses Using SVPWM Switching Technique

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Fuzzy logic sterownik wykorzystujący modulację SVPWM do kontroli szybkości silnika indukcyjnego
Języki publikacji
EN
Abstrakty
EN
This paper describes the development of a three-phase induction motor (TIM) drive speed controller. A rule-based fuzzy logic controller (FLC) is developed for TIM speed control in non-linear systems. Speed control applications are tested by conducting simulations under different operating conditions. To achieve reliable TIM operation, the space vector pulse width modulation (SVPWM) scheme is used to generate gate signals for the three-phase, two-level inverter. The SVPWM technique demonstrates excellent performance in TIM speed control. The scalar control (V/f control), which is inexpensive, simple to implement in hardware, and applicable to medium- and high-speed rated TIM applications, is used to control the developed TIM. Results show that the implementation of rule-based fuzzy with V/f control and the SVPWM technique for TIM speed control provides superior performance, which is sufficiently robust and intelligent for real-time applications.
PL
Opisano nową metodę sterowania prędkością trójfazowego silnika indukcyjnego. Wykorzystano sterownik bazujący na logice rozmytej umożliwiający sterowanie w systemach nieliniowych. Do bramkowania sygnału dwupoziomowego przekształtnika wykorzystano wektorowa modulację szerokości impulsu SVPWM. Zastosowano też skalarny przetwornik V/f.
Rocznik
Strony
133--136
Opis fizyczny
Bibliogr. 17 poz., rys., wykr.
Twórcy
autor
  • Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM)
autor
  • Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM)
autor
  • Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia (UKM)
Bibliografia
  • [1] Kailaswar S.V., Keswani R.A., Speed Control of Three Phase Induction Motor by V/f Method for Batching Motion System, Int. Journal of Engineering Research and Applications, 3 (2013), 1732-1736.
  • [2] Behera S., Das S.P., Doradla S.R., Quasi-resonant inverterfed direct torque controlled induction motor drive. Electr Power Syst Res, 77 (2007), 946-955.
  • [3] Lin C., Radial basis function neural network-based adaptive critic control of induction motors. Appl Soft Comput, 11 (2011), 3066-3074.
  • [4] Wlas M., Krzeminski Z., Toliyat H.A. Neural-Network-Based Parameter Estimations of Induction Motors. IEEE Trans. Ind. Electron. 55 (2008), 1783-1794.
  • [5] Nounou H.N., Rehman H., Application of adaptive fuzzy control to ac machines, Appl. Soft. Comput. 7 (2007), 899-907.
  • [6] Orlowska-Kowalska T., Dybkowski M., Szabat K., Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive without Mechanical Sensors, IEEE Trans. Ind. Electron. 57 (2010) 553-564.
  • [7] Suetake M., da Silva, I.N., Goedtel A., Embedded DSP-Based Compact Fuzzy System and Its Application for Induction-Motor V/f Speed Control, IEEE Trans. Ind. Electron., 58 (2011), 750-760.
  • [8] Emar W., Sarhan H., Al-Issa R., Al-Issa R., Awad, M., V/F Control of Squirrel Cage Induction Motor Drives Without Flux or Torque Measurement Dependency, Int. J. Robot. Autom., 2 (2011), 77-92.
  • [9] Santos T.H., Goedtel A., Silva S.A., Suetake M., Scalar control of an induction motor using a neural sensorless technique, Electr. Power Syst. Res., 108 (2014) 322–330.
  • [10] Woodley K.M., Li H., Foo, S.Y., Neural network modeling of torque estimation and d–q transformation for induction machine, Eng. Appl. Artif. Intell., 18 (2005), 57–63.
  • [11] Luo Y.C., Chen W.X., Sensorless stator field orientation controlled induction motor drive with a fuzzy speed controller, Comput. Math. Appl., 64 (2012), 1206–1216.
  • [12] Upasan A., Kumsuwan Y., Closed Loop Speed Control of Induction Generator with Scalar-Control Inverters, Energy Procedia, 34 (2013), 371–381.
  • [13] Guo L., Parsa L., Model reference adaptive control of fivephase IPM motorsbased on neural network, IEEE Trans. Ind. Electron., 59 (2012), 500–1508.
  • [14] Rajkumar M.V., Manoharan P.S., Ravi A., Simulation and an experimental investigation of SVPWM technique on a multilevel voltage source inverter for photovoltaic systems, Int. J. Electr. Power Energy Syst., 52 (2013), 116–131.
  • [15] Thangaraj R., Chelliah T.R., Pant M., A. Ajith, Grosan C.,Optimal gain tuning of PI speed controller in induction motor drives using particle swarm optimization, Log. J. IGPL, (2010).
  • [16] Olesiak K., Application of the fuzzy controller in the speed control system of an induction motor, Przegląd elektrotechniczny, 12 (2013), 336-339.
  • [17] Gadoue S.M., Giaouris D., Finch J.W., Artificial intelligencebased speed control of DTC induction motor drives A comparative study, Electr. Power Syst. Res., 79 (2009), 210–210.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-59dee2be-4c3d-45e6-b8f5-40994338cda8
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.