Continuous on-line identification of nonlinear plants in power systems with missing sensor measurements

A novel robust artificial neural network identifier (RANNI) model is proposed in this paper. This RANNI can continuously track the dynamics of the plant model on-line when some sensor measurements are unavailable. A static synchronous series compensator (SSSC) connected to a small power system is used as a test system to examine the validity of the proposed model. In the simulation, one sensor is assumed to be missing; simulation results show that the proposed RANNI tracks the plant dynamics with good precision during the steady state, the small disturbance, and the transient state after a large disturbance. The proposed RANNI is readily applicable to other plant models in power systems.