This paper proposes a data-centric model predictive control (MPC) for supplemental control of a DFIG-based wind farm (WF) to improve power system stability. The proposed method is designed to control active and reactive power injections via power converters to reduce the oscillations produced by the WF during disturbance conditions. Without prior knowledge of the system model, this approach utilizes the states measurements of the DFIG subsystem for control design. Therefore, a data-driven

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... l controller with a decentralized feature is developed. The learning process is based on Koopman operator theory where the unknown nonlinear dynamics of the DFIG is reconstructed by lifting the nonlinear dynamics to a linear space with an approximate linear state evolution. Extended dynamic mode decomposition (EDMD) is then applied to determine the lifted-state space matrices for the proposed Koopman-based model predictive controller (KMPC) design. The effectiveness of the proposed scheme is tested on New England IEEE 68-bus 16-machine system under three-phase fault conditions. The results ascertain the effectiveness of the proposed scheme to enhance the system damping characteristics. INDEX TERMS Koopman operator, power system stabilizers, model predictive control, double-fed induction generator, decentralized control. NOMENCLATURE A. DFIG TURBINE D. BACK-TO-BACK CAPACITOR v dc Capacitor voltage C dc Capacitance i dc Current flowing in the DC link capacitor P DC Active Power at the DC link capacitor E. RSC CONTROLLER PI Proportional integral K p1 , K i1 PI gains of power regulator PI gains of RSC current regulator K p3 , K i3 PI gains of grid voltage regulator i rd_ref Setpoint for i rd i rq_ref Setpoint for i rq v s_ref PI gains of grid voltage regulator P ref Active power setpoint P RSC RSC active power F. GSC CONTROLLER K pdg , K idg PI gains of the voltage regulator of the dc link K pg , K ig PI gains of GSC current regulator v DC_ref Voltage setpoint of the dc link i qg_ref Setpoint for i gq P GSC GSC active power