Modeling and prediction of electric arc furnace based on neural network and chaos theory

Abstract

Electric arc furnace is commonly used in iron and steel industry to produce quality steel by melting iron and steel scraps using electric arc. It represents one of the most disturbing loads in the subtransmission or transmission electric power systems. Therefore, it is necessary to build a practical model to descript the behavior of electric arc furnace in the simulation of power system. The electrical fluctuations in the electric arc furnace have proven to be chaotic in nature. This paper deals with the problem of electric arc furnace modeling using the combination of chaos theory and neural network. The radial basis function neural network is used to predict the arc voltage of arc furnace with one-step and multi-step ahead. The results can be applied to simulate the EAF load in power system and estimate the future state of arc furnace for control purpose. © Springer-Verlag Berlin Heidelberg 2005.