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Stator side active and reactive power control with improved rotor position and speed estimator of a grid connected DFIG (doubly-fed induction generator)

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  • Gayen, P.K.
  • Chatterjee, D.
  • Goswami, S.K.

Abstract

High performance vector control of DFIG (doubly-fed induction generator) in connection with wind power generation demands accurate rotor position and speed information to the controller. In this paper, the measured stator and rotor current and voltages are used to calculate the rotor position and speed based on MRAS (model reference adaptive system) control strategy. The air-gap EMF (electromotive force) is taken as the working variable. The main features of the proposed scheme meets various desirable requirements of a high performance controller e.g. good dynamic response, no requirement of flux estimation, no necessity of low frequency signal integration or usage of no differentiator for computation. Moreover, the performance of the control loop is least affected by the machine parameters variation. The proposed method is simple, reasonably accurate and can be easily applied for real time implementation. Different experiments and simulations are performed on a 3.2 kW slip ring induction machine and the results are compared with traditional schemes to validate the proposed concept.

Suggested Citation

  • Gayen, P.K. & Chatterjee, D. & Goswami, S.K., 2015. "Stator side active and reactive power control with improved rotor position and speed estimator of a grid connected DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 89(C), pages 461-472.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:461-472
    DOI: 10.1016/j.energy.2015.05.111
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    References listed on IDEAS

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    1. Pichan, Mohammad & Rastegar, Hasan & Monfared, Mohammad, 2013. "Two fuzzy-based direct power control strategies for doubly-fed induction generators in wind energy conversion systems," Energy, Elsevier, vol. 51(C), pages 154-162.
    2. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    3. Fernandez, L.M. & Garcia, C.A. & Jurado, F., 2008. "Comparative study on the performance of control systems for doubly fed induction generator (DFIG) wind turbines operating with power regulation," Energy, Elsevier, vol. 33(9), pages 1438-1452.
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    Cited by:

    1. Darvish Falehi, Ali, 2020. "An innovative optimal RPO-FOSMC based on multi-objective grasshopper optimization algorithm for DFIG-based wind turbine to augment MPPT and FRT capabilities," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    2. Yang, Bo & Yu, Tao & Shu, Hongchun & Dong, Jun & Jiang, Lin, 2018. "Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers," Applied Energy, Elsevier, vol. 210(C), pages 711-723.
    3. Rahimi, Mohsen, 2016. "Drive train dynamics assessment and speed controller design in variable speed wind turbines," Renewable Energy, Elsevier, vol. 89(C), pages 716-729.

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