Inverse optimal neural control with speed gradient for a power electric system with changes in loads

In this paper an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems, in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A reduced neural model for synchronous machine is proposed for the stabilization of nine bus system in the presence of a fault in a line of transmission with some variations in loads. � 2012 IEEE