Power system stabilization based on modular neural network architecture

Abstract

Backpropagation neural networks have recently been applied to problems in power system stabilizer modeling. When trained to respond differently to different operating conditions, these networks tend to produce interference between conflicting solutions. In recent years, modular neural network architectures have been used for problems in system identification and control. These networks learn different aspects of a problem by partitioning the data space into several different regions and are less susceptible to interference than backpropagation networks. This paper investigates the use of modular neural networks for power system stabilizer modeling. Simulation studies are performed to compare the modular neural network model of a power system stabilizer against a backpropagation model and a conventional power system stabilizer model.