Design of an adaptive power system stabilizer using recurrent neural networks

In this paper, an application of recurrent neural networks in the design of an adaptive power system stabilizer (PSS) is presented. The architecture of the proposed adaptive PSS has two recurrent neural networks: the first functions as an identifier to learn the dynamic characteristics of power plant; the second functions as a controller to damp the oscillations of power plant caused by different disturbances. In the proposed approach, the weights of the neural networks are updated online. Therefore, any new information available during actual control of the plant is considered. Simulation studies show that the proposed controller can improve the transient performance of the power plant.