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Optimal Intelligent Control for Doubly Fed Induction Generators

Author

Listed:
  • Lingqin Xia

    (Department of Additive Manufacturing, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China)

  • Guang Chen

    (Department of Additive Manufacturing, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China)

  • Tao Wu

    (Department of Additive Manufacturing, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China)

  • Yu Gao

    (Department of Additive Manufacturing, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China)

  • Ardashir Mohammadzadeh

    (Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Ebrahim Ghaderpour

    (Department of Earth Sciences, Sapienza University of Rome, Piazzale Aldo-Moro, 5, 00185 Rome, Italy)

Abstract

For the first time, a novel concept of merging computational intelligence (the type-2 fuzzy system) and control theory (optimal control) for regulator and reference tracking in doubly fed induction generators (DFIGs) is proposed in this study. The goal of the control system is the reference tracking of torque and stator reactive power. In this case, the type-2 fuzzy controller is activated to enhance the performance of the optimum control. For instance, in abrupt changes of the reference signal or uncertainty in the parameters, the type-2 fuzzy system performs a complementary function. Both parametric uncertainty and a perturbation signal are used to challenge the control system in the simulation. The findings demonstrate that the presence of a type-2 fuzzy system as an additional controller or compensator significantly enhances the control system. The root mean square error of the suggested method’s threshold was 0.012, quite acceptable for a control system.

Suggested Citation

  • Lingqin Xia & Guang Chen & Tao Wu & Yu Gao & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "Optimal Intelligent Control for Doubly Fed Induction Generators," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:20-:d:1009621
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    References listed on IDEAS

    as
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    Cited by:

    1. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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