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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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    17. Giannakis, Andreas & Karlis, Athanasios & Karnavas, Yannis L., 2018. "A combined control strategy of a DFIG based on a sensorless power control through modified phase-locked loop and fuzzy logic controllers," Renewable Energy, Elsevier, vol. 121(C), pages 489-501.
    18. 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.
    19. Xie, Wei & Zeng, Pan & Lei, Liping, 2015. "Wind tunnel experiments for innovative pitch regulated blade of horizontal axis wind turbine," Energy, Elsevier, vol. 91(C), pages 1070-1080.
    20. Medjber, Ahmed & Guessoum, Abderrezak & Belmili, Hocine & Mellit, Adel, 2016. "New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system," Energy, Elsevier, vol. 106(C), pages 137-146.
    21. Maheshwari, Zeel & Kengne, Kamgang & Bhat, Omkar, 2023. "A comprehensive review on wind turbine emulators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    22. 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.
    23. 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.
    24. 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.
    25. 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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