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Hierarchical Cascaded Takagi-Sugeno Model Predictive Control for Performance Enhancement of Doubly-fed Induction Generator-Based Wind Turbine Systems

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This paper proposes a cascaded Takagi-Sugeno Model Predictive Controller (TS-MPC) for a Doubly-fed Induction Generator (DFIG) based Wind Power Conversion System (WPCS) to maximize power extraction, maintain zero stator reactive power, and enhance power quality. For this purpose, the Takagi-Sugeno Fuzzy Logic Control (TS-FLC) is arranged in a sequential configuration with the Finite Control-Set Model Predictive Control (FCS-MPC) strategy to enhance the overall performance of the wind power system. The introduced control technique, which is applied to govern the Rotor Side Converter (RSC) of the DFIG, consists of two cascaded control loops for achieving Maximum Power Point Tracking (MPPT). The innermost control loop is implemented to regulate the d-q axis rotor currents using FCS-MPC strategy. Meanwhile the outermost control loop is employed to regulate the DFIG’s rotational speed pursuant to the Tip Speed Ratio MPPT (TSR-MPPT) control framework using the TS-FLC, thus improving the predictive accuracy and control effectiveness.  To validate the performance of the devised control scheme, a numerical simulation of a 1.5MW DFIG based WPCS was conducted using MATLAB/Simulink software. The simulation results demonstrate that the proposed cascaded TS-MPC not only outperforms the cascaded PI-MPC in terms of superior adaptability to nonlinearities and varying wind conditions—thanks to the inherent flexibility of TS-FLC—but also in various performance metrics, including response time, steady-state error, and total harmonic distortion (THD).Furthermore, while FCS-MPC approaches are often criticized for computational complexity, the TS-FLC structure enhances real-time feasibility by reducing computational overhead compared to conventional FLC methods. These findings reinforce the practical viability of TS-MPC for large-scale wind energy applications and indicate the effectiveness of the proposed control scheme.
Title: Hierarchical Cascaded Takagi-Sugeno Model Predictive Control for Performance Enhancement of Doubly-fed Induction Generator-Based Wind Turbine Systems
Description:
This paper proposes a cascaded Takagi-Sugeno Model Predictive Controller (TS-MPC) for a Doubly-fed Induction Generator (DFIG) based Wind Power Conversion System (WPCS) to maximize power extraction, maintain zero stator reactive power, and enhance power quality.
For this purpose, the Takagi-Sugeno Fuzzy Logic Control (TS-FLC) is arranged in a sequential configuration with the Finite Control-Set Model Predictive Control (FCS-MPC) strategy to enhance the overall performance of the wind power system.
The introduced control technique, which is applied to govern the Rotor Side Converter (RSC) of the DFIG, consists of two cascaded control loops for achieving Maximum Power Point Tracking (MPPT).
The innermost control loop is implemented to regulate the d-q axis rotor currents using FCS-MPC strategy.
Meanwhile the outermost control loop is employed to regulate the DFIG’s rotational speed pursuant to the Tip Speed Ratio MPPT (TSR-MPPT) control framework using the TS-FLC, thus improving the predictive accuracy and control effectiveness.
  To validate the performance of the devised control scheme, a numerical simulation of a 1.
5MW DFIG based WPCS was conducted using MATLAB/Simulink software.
The simulation results demonstrate that the proposed cascaded TS-MPC not only outperforms the cascaded PI-MPC in terms of superior adaptability to nonlinearities and varying wind conditions—thanks to the inherent flexibility of TS-FLC—but also in various performance metrics, including response time, steady-state error, and total harmonic distortion (THD).
Furthermore, while FCS-MPC approaches are often criticized for computational complexity, the TS-FLC structure enhances real-time feasibility by reducing computational overhead compared to conventional FLC methods.
These findings reinforce the practical viability of TS-MPC for large-scale wind energy applications and indicate the effectiveness of the proposed control scheme.

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