Enhancement of SVC performance in electric arc furnace for flicker suppression using a Gray-ANN based prediction method

Delays in reactive power measurement and thyristor ignition limit SVC performance in flicker mitigation of electric arc furnaces (EAFs). To overcome this limitation, prediction methods can be employed to forecast the EAF reactive power for half cycle ahead, used as a reference signal of the SVC. The utilized prediction methods in this area can be divided into linear and black-box approaches. However, the linear approaches cannot extract the nonlinear governed relations, and using a black-box model is not efficient for linear relations. A Gray-ANN method is proposed here to take the advantages of the two mentioned approaches. Results from indices based on actual records of Mobarakeh Steel Company confirm superiorities of the proposed method over previously utilized prediction methods in this application. Furthermore, SVC's flicker mitigation ability is evaluated using the actual data. The results confirm the significant reduction of flicker compared with the regular system.