Robust neural controllers for power system based on new reduced models

This paper presents an advanced control method for the stabilization of Electric power systems. This method is a decentralized control strategy based on a set of neural controllers. Essentially, the large-scale power system is decomposed into a set of subsys-tems in which each one is constituted by a single ma-chine connected to a variable bus. For each subsystem, a neural controller is designed to respond to a perfor-mance index. The neural controller is a feed-forward multi-layered one. Its training method is accomplished for different rates of desired terminal voltage and is based on the perturbed electrical power system model. For a single machine, the synaptic weights of corre-sponding neural controller are adjusted to force the ma-chine outputs to converge into expected one obtained by the load flow program. To evaluate the performance and effectiveness of the proposed control method, it has been applied to the WSCC power system under severe operating conditions. The obtained results compared to the ones of conventional controllers proved the high quality of the proposed controller in terms of tran-sient stability and voltage regulation of the considered electrical power system.