AN ONLINE SELF-LEARNING POWER-SYSTEM STABILIZER USING A NEURAL-NETWORK METHOD

Based on the extensive theoretical analysis of self-learning algorithm a novel on-line neural network self-learning algorithm is proposed. T his algorithm aims to learn the inverse dynamics of a controlled syste m. Samples can be easily obtained by the measurements. A reference mod el or a given orbit is used to generate ideal system responses. A sche me for on-line real-time implementation of such a controller is given. The proposed algorithm has been used to design a self-learning power system stabilizer. Simulation results show that the proposed self-lear ning neural network based PSS is very effective in damping out the low er frequency oscillations.