Estimation of electric arc furnace parameters using multi-objective optimization method and genetic algorithm (Case study: Khuzestan Steel Plant)

Electric arc furnace is one of the major loads in the power grid and which, due to the fact that it is a non-linear and random load, causes many disturbances in the grid. The arc furnace causes problems such as harmonic, sub-harmonic, voltage imbalance, flicker voltage and voltage fluctuation and current on the power grid. Therefore, in order to reduce the problems caused by electric arc furnaces, it is necessary to install compensating equipment such as SVC next to them. In order to analyze the effects of electric arc furnace on the network, calculate the capacity and type of compensator required for each arc furnace, first the appropriate furnace model must be extracted and performed in the simulation software environment. After performing the simulation in the software environment and studying the furnace behavior, a suitable compensator will be designed for the arc furnace. Given that the arc furnace has a nonlinear and random behavior, it is difficult to provide a suitable and accurate model for studying the power quality phenomena caused by the furnace on the network. The purpose of this article is to obtain a suitable model for the electric arc furnace of Khuzestan Steel Company. For this purpose, the voltage and current of the furnace transformer are first taken. Then, the genetic algorithm is used to adapt the desired models and obtain the optimal model parameters and the best model is selected.