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    This study introduces an efficient sampled-data controller for stabilizing wind energy systems. The novel hybrid approach ensures system stability using Lyapunov theory and linear matrix inequalities.

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    Area of Science:

    • Control Systems Engineering
    • Renewable Energy Systems
    • Electrical Engineering

    Background:

    • Surface-mounted permanent magnet synchronous generators (PMSGs) are crucial for wind energy conversion systems (WECS).
    • Stabilization of WECS is essential for reliable power generation.
    • Existing controllers often use either continuous or discrete signals, limiting performance.

    Purpose of the Study:

    • To design an efficient sampled-data controller for stabilizing PMSG-based WECS.
    • To develop a controller integrating both continuous (plant) and discrete (control) signals.
    • To analyze the dynamical characteristics and ensure the stability of the closed-loop system.

    Main Methods:

    • Modeling the nonlinear PMSG system using the Takagi-Sugeno (T-S) fuzzy approach.
    • Applying Lyapunov stability theory to guarantee system stability.
    • Deriving sufficient stability conditions using solvable linear matrix inequalities (LMIs).

    Main Results:

    • A novel sampled-data controller integrating continuous and discrete signals was designed.
    • The T-S fuzzy model effectively represented the PMSG dynamics.
    • LMIs provided sufficient conditions for stability and stabilization, validated by numerical evaluations.

    Conclusions:

    • The proposed sampled-data controller scheme effectively stabilizes PMSG-based WECS.
    • The hybrid continuous-discrete signal approach offers superior performance.
    • The Lyapunov stability theory and LMIs provide a robust framework for WECS stabilization.