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In this paper is presented a control system that is based on the proportionalderivative controller for modeling and simulation of the electric arc furnace focusing on the controller’s loop. The proposed controller is used to modify the position of the electrodes of the electric arc furnace in order to minimize the effect of the disturbances that can appear in the process during its functioning. With the proposed controller can be obtain the maximum power that can be used during functioning of the electric arc furnace. Also, the proposed control system can be used to obtain a specific active power for the electric arc that appears in the functioning of the electric arc furnace. In this paper are presented the steps used to tune the proportional and derivative gains of the controller. Used mathematical model of the electric arc is based on the voltage-current characteristic of the electric arc that simulates the functioning of the electric arc from the electric arc furnace. The model is integrated in the control system implementation. The simulations are implemented using Simulink toolbox from Matlab software. Taking into consideration obtained results during simulation are selected the best values for the gains of the proportional-derivative controller in order to obtain performance optimization of the electric arc furnace. For the selected gains of the proportional-derivative controller is presented the response of the system that is tested in a closed loop.

The paper presents the results of the modeling and the prediction of the total harmonic distortion (THD) of the current and the voltage for a nonlinear high power load. Modeling was performed using intelligent techniques based on neural networks and fuzzy inference. To achieve this, data were measured in the electrical installation of a non-linear electrical load, in this case a high-power electric arc furnace. These data were measured over the entire duration of a steel charge. The measured data was used to train a neuro fuzzy adaptive system. Following training, tests with different architectures have been performed with the neuro fuzzy adaptive system. The modeling and prediction results are useful in designing harmonic current filters. The presence of these harmonic currents decreases productivity and affects the quality of power.

In this research is proposed a control system used to obtain the maximum or a specific power of an electric arc. The plant taken into consideration is a three-phase electric arc furnace that is supplied from a special transformer. The process is modeled using an exponential model of the electric arc. By simulating the model in Matlab/Simulink environment the arc powers for five of the transformer's taps were obtained. Also by ranging the arc length value in a specific domain was obtained the optimal length to achieve the maximum power for a specific tap. In the closed-loop control system is used a fuzzy controller. In order to test the system performance the control system is tested in two cases. Fist case is at injection of disturbances in the process, the transformer is set on a specific tap and the set-point value is varied in a specific domain. The second case in when are injected disturbances in the process, the transformer tap is changed and the set-point value is kept constant at a power representing the maximum arc power for each of the transformer's tap.

This paper presents a study on the possibility of making a prediction for the electric current of nonlinear high power loads. One of the most significant nonlinear loads is the electric arc furnace (EAF). EAF is widely used in steel production and introduces many disturbances in the electric power supply. The prediction of the electric current provides information about the process that is useful in process control. For making the prediction recurrent Narx neural networks were used. The prediction was made using Matlab Neural Network Toolbox. To train the artificial neural network and to perform the prediction, measurements of currents and voltages were used. The measurements were made from the secondary transformer that supplies an EAF.

In this paper an electric arc model is presented, model which depended of the desired active power of the electric arc. The model was implemented in PSCAD — EMTDC as a custom embedded component defined by the user. The components are designed, implemented and can be saved into a PSCAD library in order to be used in simulation of electric arc schemes. The performances of the model were also studied using PSCAD — EMTDC.

This paper presents a current control for a three-phase electric arc furnace. In order to do this, a model of the electric arc was used. Using this model, the waveforms for the arc current, arc voltage and voltage-current characteristic of the electric arc were illustrated for different arc lengths. Also, an arc current control algorithm was proposed. This algorithm is used for the designing and implementation of a fuzzy controller which has the current deviation as input and electrode speed as output. The response of the closed-loop system is presented for a setpoint arc current.

The aim of this research is to study parameters influence in the electric arc modeling. In order to perform this, a study of the electric arc behavior was made by implementing three models of the electric arc in the Matlab/Simulink environment. Furthermore, graphical user interfaces in Matlab were developed, the purpose being to obtain the dynamic voltage-current characteristic of the electric arc. Both, simulated and measured data were compared and presented in this research. From an AC electric arc furnace, having the capacities of 100 tones, fed by medium voltage, the measured data were acquired using the data acquisition board, ADA3100.

This paper presents an analysis regarding the modeling of the behavior for a three-phase electric arc furnace installation. Therefore, a block diagram is implemented in Simulink that represents the modeling of the entire electric arc furnace installation. This block diagram contains also the modeling of the electric arc which is the element that makes the electric arc furnace behaving as a nonlinear load. The values for the model parameters of the electric arc furnace installation are like the ones from the real installation taken into consideration. Other model parameters are the electric arc model ones. In order to study the influence of the parameters of the electric arc models, it is developed a Matlab program that contains the graphical user interfaces. These interfaces make connection with the models of the electric arc implemented in Simulink. The interfaces allow the user to modify parameters for each of the electric arc model. Current and voltage of the electric arc are the variables taken into account to study the influence of the parameters on the electric arc models. Waveforms for voltage and current of the electric arc are illustrated when a parameter of the model is modified in order to analyze the importance of this parameter on the electric arc model. Also, for each of the models is presented the voltage-current characteristic of the electric arc because this characteristic gives information about the behavior of the electric arc furnace installation.

In this paper it is performed a numerical study of the voltage-current characteristic of an electric arc. To predict voltages and currents values, a multi-layer perceptron Artificial Neural Networks was used under the Matlab 2012 environment. The study is based on actual recorded data obtained from a 100 tones AC Electric Arc Furnace. Results obtained by simulation are compared with the measured one.

This paper presents a study regarding the experimental validation of a mathematical model used to simulate the electric arc behavior, taking into consideration a technological phase of the electric arc furnace process. In order to do this, the mathematical model of the electric arc was implemented in Simulink software. In this paper were presented results obtained in the meltdown stage of the charging material. This model can be useful from the point of view of power quality analysis and electric arc control. Results obtained by simulation were compared with measured data.

The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.