Online learning recurrent neural network stabilization controller for multi-machine power system

This paper presents an online learning recurrent neural network stabilization controller to improve the transient stability of a power system. Since the proposed recurrent neural network is of the online tuning type, it can stabilize the power system for different system parameters, operating conditions, and fault point. The proposed controller robustness and effectiveness in damping power system oscillations are illustrated through simulations.