Power system stabilizer based on inverse dynamics using an artificial neural network

Abstract A stable power system stabilizer (PSS) based on the inverse dynamics of the controlled system using an artificial neural network (ANA) is suggested to enhance the dynamic performances of a power system. First, an output feedback control law is driven with some conditions satisfied, which guarantees the internal stability and robustness against the asymptotically stable external disturbances. Then the control law is implemented using the inverse dynamics of the controlled plant. The inverse dynamics of the controlled plant is identified by an ANN, inverse dynamics neural network (IDNN), off-line. The pole-shifting technique and a scaling factor are introduced for the control system to meet the conditions for internal stability and robustness. The proposed controller is applied to a typical single-machine infinite-bus power system. Simulation results under various operation conditions are given which show that the proposed controller damps the low frequency oscillation in an efficient manner.