Implementation of artificial intelligence technique to model arc furnace responses

Random variations of the bus voltage and power consumption of an electric arc furnace (EAF) have a significant impact on power generation equipment, transient stability of the power system network and power quality to other interconnected loads. Therefore, an accurate representation of the load's dynamic behaviour under various system disturbances is very important. This paper presents an arc furnace model using adaptive neuro-fuzzy inference system (ANFIS) in order to capture random, non-linear and time-varying load pattern of an arc furnace. To evaluate the performance of the proposed model, several case studies are presented where the outputs of the proposed model are compared with the data recorded in the real metallurgical plant.