PARAMETER CALIBRATION FOR ELECTRIC ARC FURNACE MODELS USING SIMULATION AND NEURAL NETWORKS PARAMETER CALIBRATION FOR ELECTRIC ARC FURNACE MODELS USING SIMULATION AND NEURAL NETWORKS

Abstract

Electric arc furnaces provide a relatively simple way for melting metals. They are used in the production of highly purified steel, aluminium, copper and other metals. However, they are considered the most damaging load for electrical power systems. It is very important, therefore, to have arc furnace models that can determine the behavior of this type of load with a high degree of accuracy. In this way, it would be possible to assess the impact in terms of power quality indices for the power system to which they are connected. When using electric arc furnace models in practice, a key issue is the calibration of the model’s parameters. In this paper, we show a procedure for calibrating all the parameters of an AC electric arc furnace model using real measurements of voltages and currents. A multilayer neural network is used as an emulator of the electric arc furnace model. The neural network is trained using data obtained from the simulation of the electric arc furnace model implemented in Matlab®-Simulink®. Once the network is trained, the parameters of interest are obtained by solving an inverse problem. The results obtained show a maximum percentage error of 4.1% for the rms value of the current involved in the electrical arc.