CALIBRACIÓN DE LOS PARÁMETROS DE UN MODELO DE HORNO DE ARCO ELÉCTRICO EMPLEANDO SIMULACIÓN Y REDES NEURONALES
CALIBRACIÓN DE LOS PARÁMETROS DE UN MODELO DE HORNO DE ARCO ELÉCTRICO EMPLEANDO SIMULACIÓN Y REDES NEURONALES
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RESUMEN
El horno de arco eléctrico proporciona un medio relativamente simple para la fusión de metales. Se utiliza en la producción de acero de alta pureza, aluminio, cobre, plomo, entre otros metales. Sin embargo, los hornos de arco son considerados como la carga más nociva para el sistema eléctrico de potencia. Por consiguiente, resulta de gran importancia contar con modelos de horno de arco que permitan determinar con alto grado de aproximación el comportamiento de este tipo de carga, puesto que se podría evaluar su impacto en términos de índices de calidad de energía para el sistema de potencia al cual se conecten. Uno de los principales problemas que surge al utilizar los modelos matemáticos de arco eléctrico consiste en la calibración de los parámetros que describen la dinámica del modelo. En este documento se muestra un procedimiento para calibrar todos los parámetros de un modelo de horno de arco eléctrico de corriente alterna, dadas mediciones reales de tensiones y corrientes. Se utiliza una red neuronal multicapa como emulador del modelo del horno. La red neuronal se entrena empleando datos de simulación obtenidos del modelo del horno implementado en el entorno Matlab®-Simulink®. Una vez entrenada la red, los parámetros de interés se obtienen resolviendo un problema inverso. Los resultados obtenidos muestran un error máximo de 4,1 % en el valor eficaz de las corrientes del arco eléctrico.
ABSTRACT
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.
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