NEURAL NETWORK PREDICTIVE CONTROL BASED POWER SYSTEM STABILIZER

The present paper 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 used 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.