Determining topological changes and critical load levels of a power system by means of artificial neural networks

Abstract

In this paper, an investigation is done of the possibilities of implementing multi-layered artificial neural networks in analyzing the dynamic stability of an power system during load and topology changes. To solve this problem, a multi-layered neural network is used whose inputs are the state vector components, and whose outputs are encoded line outages and the real component of dominant eigenvalues of the system state matrix of the power system. The neural network is trained through the error back-propagation method. The proposed methodology is tested on an power system with ten nodes and four generators. The obtained results indicate the attractiveness of “on-line” application possibilities of multi-layered neural networks in order to efficiently evaluate the stability of an power system during conditions of load and topology change. © 1997 Taylor & Francis.