A predictive KH-based model to enhance the performance of industrial electric arc furnaces

This paper develops a new predictive approach to improve the static VAr compensator (SVC) performance in the electric arc furnaces (EAFs). The proposed method models the reactive power consumption pattern in the EAF for a half-cycle ahead to improve the SVC compensation process. Given this, a new nonparametric approach based on lower upper bound estimation method and support vector regression (SVR) is developed to construct prediction intervals (PIs) around the reactive power consumption pattern in the SVC. The proposed method makes use of the PI concept to model the uncertainties of reactive power and, thus, avoid the flicker issues. Owing to the high complexity and nonlinearity of the proposed problem, a new optimization method based on the krill herd (KH) algorithm is proposed to adjust the SVR setting parameters, optimally. Also, a three-stage modification method is suggested to increase the krill population and avoid the premature convergence. The feasibility and performance of the proposed method are examined using experimental data gathered from the Mobarakeh Steel Company, Iran.