Resumo

ALVAREZ LOPEZ, Mauricio Alexánder; HENAO BAENA, Carlos Alberto  e  MARULANDA DURANGO, Jesser James. CALIBRATION OF PARAMETERS FOR ELECTRIC ARC FURNACE MODEL USING SIMULATION AND NEURAL NETWORKS. Rev.EIA.Esc.Ing.Antioq [online]. 2014, n.22, pp.39-50. ISSN 1794-1237.

Electric arc furnace provides 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 more damaging load for the power system. It is very important, therefore, to count on arc furnace models for determining with high degree of accuracy the performance of this type of load. In this way, it would be possible to assess the impact in terms of power quality indices for the power system to which they might be connected. When using electric arc furnace models in practice, a key issue is the calibration of the parameters of the model. 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. It uses a multilayer neural network 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. Results obtained show a maximum percentage error of 4.1 % for the rms value of the current involved in the electrical arc.

Palavras-chave : Electric Arc Furnace; Calibration of Parameters; Neural Networks; Latin Hypercube; Computer Emulation; Fornos de arco; Calibração de parâmetros; Redes neurais; Latin Hypercube; Emulação de computador.

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