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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.

Summary An improved adaptive neuro‐sliding mode control scheme that incorporated a completely adaptive radial basis function (RBF) neural network into a sliding‐mode controller to approximate the control of a static synchronous series compensator device is presented in this paper. The proposed nonlinear controller does not require the full state of the nonlinear system nor the full knowledge of the bounds of uncertainty, disturbance, and approximation error. It makes use of a reduced number of hidden units, and the weights, centers, and widths are all updated through an on‐line learning mechanism. The effectiveness of the proposed control scheme has been verified on a three‐machine, nine‐bus Institute of Electrical and Electronics Engineers (IEEE) power system with a static synchronous series compensator in MATLAB/Simulink software. The stability of the system and adaptive control laws have been proven using the Lyapunov stability theorem. Simulation results show that the robustness of the proposed controller can satisfactorily alleviate the chattering effects as well as the internal and external perturbations considered.