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Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)

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  • Belmokhtar, K.
  • Doumbia, M.L.
  • Agbossou, K.

Abstract

This paper presents a novel FLC MPPT (fuzzy logic sensorless maximum power point tracking) method for WECS (wind energy conversion systems). The proposed method greatly reduces the speed variation range of the wind generator which leads to the downsizing the PWM (pulse width modulation) back-to-back converters by approximately 40% in comparison with conventional techniques. The method also increases the system's reliability by reducing the converter losses. Firstly, a MRAS (model reference adaptive system) based on fuzzy logic technique is used to estimate the DFIG (doubly-fed induction generator) rotor's speed. Then, a FLC MPPT (Fuzzy Logic Maximum Power Point Tracking) method is applied to provide the reference electromagnetic torque. Subsequently, in order to achieve the overall sensorless MPPT technique, the wind power is approximated from estimated generator speed and the reference of electromagnetic torque. Finally, the wind speed is estimated from the mechanical power using a fuzzy logic technique. The proposed control method has been applied to a WTG (wind turbine generator) driving a 3.7 kW DFIG in variable speed mode. In order to validate the simulation results, experimental tests have been performed on a 3.7 kW test bench, consisting of a DFIG and DC motor drive.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:76:y:2014:i:c:p:679-693
    DOI: 10.1016/j.energy.2014.08.066
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    12. Fathabadi, Hassan, 2017. "Novel standalone hybrid solar/wind/fuel cell/battery power generation system," Energy, Elsevier, vol. 140(P1), pages 454-465.
    13. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    14. Fathabadi, Hassan, 2016. "Novel highly accurate universal maximum power point tracker for maximum power extraction from hybrid fuel cell/photovoltaic/wind power generation systems," Energy, Elsevier, vol. 116(P1), pages 402-416.
    15. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    16. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    17. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
    18. Fantino, Roberto & Solsona, Jorge & Busada, Claudio, 2016. "Nonlinear observer-based control for PMSG wind turbine," Energy, Elsevier, vol. 113(C), pages 248-257.
    19. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    20. Maheshwari, Zeel & Kengne, Kamgang & Bhat, Omkar, 2023. "A comprehensive review on wind turbine emulators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    21. Fathabadi, Hassan, 2016. "Maximum mechanical power extraction from wind turbines using novel proposed high accuracy single-sensor-based maximum power point tracking technique," Energy, Elsevier, vol. 113(C), pages 1219-1230.
    22. Fahimeh Shiravani & Jose Antonio Cortajarena & Patxi Alkorta & Oscar Barambones, 2022. "Generalized Predictive Control Scheme for a Wind Turbine System," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    23. 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.
    24. Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.

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