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Low Sensitivity Predictive Control for Doubly-Fed Induction Generators Based Wind Turbine Applications

Author

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  • Mohamed Abdelrahem

    (Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt
    Institute for Electrical Drive Systems and Power Electronics (EAL), Technische Universität München (TUM), 80333 Munich, Germany)

  • Christoph Hackl

    (Department of Electrical Engineering and Information Technology, Munich University of Applied Sciences, 80335 München, Germany)

  • Ralph Kennel

    (Institute for Electrical Drive Systems and Power Electronics (EAL), Technische Universität München (TUM), 80333 Munich, Germany)

  • Jose Rodriguez

    (Faculty of Engineering, University Andrés Bello, Santiago 8370146, Chile)

Abstract

In this paper, a deadbeat predictive control (DBPC) technique for doubly-fed induction generators (DFIGs) in wind turbine applications is proposed. The major features of DBPC scheme are its quick dynamic performance and its fixed switching frequency. However, the basic concept of DBPC is computing the reference voltage for the next sample from the mathematical model of the generator. Therefore, the DBPC is highly sensitive to variations of the parameters of the DFIG. To reduce this sensitivity, a disturbance observer is designed in this paper to improve the robustness of the proposed DBPC scheme. The proposed observer is very simple and easy to be implemented in real-time applications. The proposed DBPC strategy is implemented in the laboratory. Several experiments are performed with and without mismatches in the DFIG parameters. The experimental results proved the superiority of the proposed DBPC strategy over the traditional DBPC technique.

Suggested Citation

  • Mohamed Abdelrahem & Christoph Hackl & Ralph Kennel & Jose Rodriguez, 2021. "Low Sensitivity Predictive Control for Doubly-Fed Induction Generators Based Wind Turbine Applications," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9150-:d:615071
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    References listed on IDEAS

    as
    1. Ali Mohamed Eltamaly & Mamdooh Al-Saud & Khairy Sayed & Ahmed G. Abo-Khalil, 2020. "Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique," Sustainability, MDPI, vol. 12(9), pages 1-14, April.
    2. Mohamed Abdelrahem & Christoph M. Hackl & José Rodríguez & Ralph Kennel, 2020. "Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems," Energies, MDPI, vol. 13(18), pages 1-15, September.
    3. Omid Sadeghian & Sajjad Tohidi & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2021. "A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine," Sustainability, MDPI, vol. 13(2), pages 1-39, January.
    4. Tohidi, Sajjad & Behnam, Mohammadi-ivatloo, 2016. "A comprehensive review of low voltage ride through of doubly fed induction wind generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 412-419.
    5. Baroudi, Jamal A. & Dinavahi, Venkata & Knight, Andrew M., 2007. "A review of power converter topologies for wind generators," Renewable Energy, Elsevier, vol. 32(14), pages 2369-2385.
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    Cited by:

    1. 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.

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