Abstract
The present paper proposes a model of fuzzy logic control of a doubly fed asynchronous machine (DFAM). First, a mathematical model of DFAM, written in an appropriate d-q reference frame, is established to investigate the results of simulations. In order to control the rotor currents of DFAM, a torque tracking control law is synthesized using PI controllers; the stator side power factor is controlled at a unity level. Then, artificial intelligent controls, such as fuzzy logic control, are applied. The simulated performances are then compared to those of a classical PI controller. Results obtained, in Matlab/Simulink environment, show that the fuzzy control is more robust i.e. has a superior dynamic performance and, hence, is found to be a suitable replacement of the conventional PI controller for a high performance drive applications.
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Arul I, Karthikeyan M, Krishnan N, Muthukumar S (2012) A design and modeling techniques for maximum power optimization on wind electrical power system with variable speed generation using neuro fuzzy. Int J Emerg Technol Adv Eng, 2(9):272–277
Barbade SA, Kasliwal P (2012) Neural network based control of doubly-fed induction generator in wind power generation. IOSR J Electr Electron Eng, IOSRJEEE 1(6):35–40
Brekken TKA (2005) A novel control scheme for a doubly-fed induction wind generator under unbalanced grid voltage condition
Chaiba A, Abdessemed R, Bendaas ML, Dendouga A (2005) Performances of torque tracking control for doubly fed asynchronous motor using PI and fuzzy logic controllers. J Electr Eng, JEE 5(2):25–30
Eisenhut C, Krug F, Schram C, Klockl B (2007) Wind turbine model for system simulations. Near cut-in wind speed. IEEE Trans, Energy Convers 22:414–420
Hanseen AD (2004) Generator and power electronics for wind turbine. In: Ackermann T (ed) Wind power in power systems. Wiley, Chichester
Hellendoorn H (1990) Closure properties of the compositional rule of inference. Fuzzy Sets Syst 35:163–183
Nait-kaci B, Doumbia ML (2009) Active and reactive power control of a doubly fed induction generator for wind applications, IEEE
Peterson A (2005) Analysis, modeling and control of doubly fed induction generators for wind turbines. In: Energy and environment. PhD Dissertation thesis, Chalmers University of Technology, Goteborg
Protsenko K, Xu D (2008) Modeling and control of brushless doubly-fed induction generators in wind energy applications. Department of Electrical and Computer Engineering Ryerson University, Toronto
Qingding G, Lime W, Rueful L (1997) Robust fuzzy variable structure control of PMLSM servo system, In: IEEE international conference on intelligent processing systems, Beijing, pp 675–679
Runkler TA (1997) Selection of appropriate defuzzification methods using application specific properties. IEEE Trans Fuzzy Syst 5:72–79
Shen B, Mwinyiwiwa B, Zhang Y, Obi B-T (2009) Sensor less maximum power point tracking of wind by DFIG using rotor position phase lock loop (PLL). IEEE Trans Power Electron 24(4):942–951
Takagi T, Surgeon M (1983) Derivation of fuzzy control rules from human operator’s control actions. In Proceedings IFAC symposium on fuzzy information, knowledge representation and decision analysis, pp 55–60
Tapia A, Tapia G, Ostolaza JX (2003) Modeling and control of a wind turbine driven doubly fed induction generator. IEEE 18(2):194–204
Xu WC (1995) Torque and reactive power control of a doubly-fed induction machine by position sensor less scheme. IEEE Trans Ind Appl 31(3):636–642
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Annexure
Annexure
Wound rotor induction machine parameters:
Nominal power \(P_{n}\) = 1.5 MW
Stator frequency \(f_{s}\) = 50 Hz
Stator resistance \(R_{s}\) = 0.12 Ω
Stator inductance \(L_{s}\) = 0.0205 H
Rotor resistance \(R_{r}\) = 0.021 Ω
Rotor inductance \(L_{r}\) = 0.0204 H
Mutual inductance \(L_{m}\) = 0.0169 H
Inertia constant J = 1000 kg m−2
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Guediri, A.K., Ben Attous, D. Modeling and fuzzy control of a wind energy system based on double-fed asynchronous machine for supply of power to the electrical network. Int J Syst Assur Eng Manag 8 (Suppl 1), 353–360 (2017). https://doi.org/10.1007/s13198-015-0367-1
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DOI: https://doi.org/10.1007/s13198-015-0367-1