Local Signal based Supplementary Excitation Controller for Damping Inter-area Oscillations through Recurrent Neural Networks

In order to effectively restrain inter-area oscillations in power systems, a local measurement based neural excitation controller is proposed to generate global stable signal. This is to replace the global measurement based power system stabilizer (GPSS). The proposed neural controller is constructed by two recurrent neural networks: a recurrent neural identifier (RNID) and a recurrent neural controller (RNCT). Non-measurable global dynamics in large-scale multi-machine power systems is estimated by the RNID and is provided to RNCT in order to generate global stable signals for a higher hierarchy of supplementary excitation control. Both RNID and RNCT are trained offline first to approximate the function of GPSS before online application. Simulation results based on Kundur´s 2-area 4-machine power system model proved the effectiveness of the proposed local signal based neural identifier and controller in damping inter-area oscillations.