Power system stabilization using a free model based inverse dynamic linear controller

Abstract

This paper presents an implementation of power system stabilizer using inverse dynamic linear controller. Traditionally, multilayer neural network is used for a universal approximator and applied to a system as a neurocontroller. In this case, at least two neural networks are required and continuous tuning of the neurocontroller is required. Moreover, training of the neural network is required, considering all possible disturbances, which is impractical in real situation. In this paper, an inverse dynamic linear model (IDLM) is introduced to avoid this problem. The inverse dynamic linear controller consists of an IDLM and an error reduction linear model (ERLM). It does not require much time to train the IDLM. Once the IDLM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for a one machine and infinite-bus power system for various operating conditions.