Paper Info
Title
A Predictive KH-Based Model to Enhance the Performance of Industrial Electric Arc Furnaces
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.
Year
DOI
Venue
2019
10.1109/tie.2018.2880710
IEEE Transactions on Industrial Electronics
Keywords
Field
DocType
Reactive power,Static VAr compensators,Voltage fluctuations,Upper bound,Predictive models,Estimation,Sociology
Population,Flicker,Premature convergence,Control theory,Support vector machine,Nonparametric statistics,AC power,Prediction interval,Static VAR compensator,Engineering
Journal
Volume
Issue
ISSN
66
10
0278-0046
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Abdollah Kavousi-Fard126831.99
Wencong Su225427.89
Jin Tao300.34
Ameena Al-Sumaiti444.99
Haidar Samet5439.68
Abbas Khosravi650160.11