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

Abdollah Kavousi-Fard, Wencong Su, Tao Jin, Ameena Saad Al-Sumaiti, Haidar Samet, Abbas Khosravi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageBritish English
Article number8542952
Pages (from-to)7976-7985
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number10
DOIs
StatePublished - Oct 2019

Keywords

  • Electric arc furnace (EAF)
  • prediction
  • reactive power compensation
  • static VAr compensator (SVC)
  • uncertainty

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