A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs

Li Zeng, Tian Xia, Salah K. Elsayed, Mahrous Ahmed, Mostafa Rezaei, Kittisak Jermsittiparsert, Udaya Dampage and Mohamed A. Mohamed
Additional contact information
Li Zeng: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Tian Xia: Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China
Salah K. Elsayed: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Mahrous Ahmed: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Mostafa Rezaei: Queensland Micro- and Nanotechnology Centre, Griffith University, Brisbane 4111, Australia
Kittisak Jermsittiparsert: College of Innovative Business and Accountancy, Dhurakij Pundit University, Bangkok 10210, Thailand
Udaya Dampage: Faculty of Engineering, Kotelawala Defence University, Ratmalana 10390, Sri Lanka
Mohamed A. Mohamed: Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt

Sustainability, 2021, vol. 13, issue 11, 1-17

Abstract: A static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM’s parameters during training. The simulation’s outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs.

Keywords: static VAR compensator; reactive power; teaching–learning algorithm; electric arc furnace; secured model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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