The present study investigates the power system stabilizer based on neural predictive control for
improving power system dynamic performance over a wide range of operating conditions. In this study a design
and application of the Neural Network Model Predictive Controller (NN-MPC) on a simple power system
composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The
synchronous machine is represented in detail, taking into account the effect of the machine saliency and the
damper winding. Neural network model predictive control combines reliable prediction of neural network
model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt
optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using
performance system of the proposed controller is compared with the system performance using conventional
one (PID controller) through simulation studies. Digital simulation has been carried out in order to validate the
effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results
demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small
settling time.