This paper presented an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems in the presence of external disturbances and parameter uncertainties, for a power electric system with different types of faults in the transmission
lines including load variations. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman
filter (EKF) based algorithm. It is well known that electric power grids are considered as complex systems due to their interconections
and number of state variables; then, in this paper, a reduced neural model for synchronous machine is proposed for the stabilization of nine
bus system in the presence of a fault in three different cases in the lines of transmission