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This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

An adaptive nonlinear controller for transient stability and voltage regulation of power systems based DFIG in multimachine configuration is presented using a standard third order dynamical model of the DFIG. Finite time estimators for the unmeasurable time derivative of the quadrature component of the DFIG stator current, mechanical input, unknown direct axis transient open circuit time constant (function of the rotor resistance) are presented. The main feature of the proposed control scheme is its robustness with respect to large perturbations and parameter variations. Numerical results are presented to illustrate the performance of the proposed control scheme and its robustness properties. Index Terms-Time-varying parameter estimation, high order sliding mode differentiator, transient stabilization, voltage regulation.