Indirect adaptive internal neuro-control for a static synchronous series compensator (SSSC) connected to a power system

A novel indirect adaptive internal neuro-controller (IAINC) using two radial basis function neural networks (RBFNN) and actual signals is presented in this paper for controlling a static synchronous series compensator (SSSC) with energy storage devices, which is connected to a small power system. The one RBF network is the neuro-identiiler to identify the dynamics of the plant model at all times, and the other RBF network is the neuro-controller to drive the plant outputs to the desired values. The proposed IAINC needs no mathematical model of the SSSC or the power network. Simulation results show that the IAINC offers improved transient performances over the conventional PI controllers used by the SSSC over a wide range of system operating conditions.