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

Author

Listed:
  • 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
    Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

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.

Suggested Citation

  • Li Zeng & Tian Xia & Salah K. Elsayed & Mahrous Ahmed & Mostafa Rezaei & Kittisak Jermsittiparsert & Udaya Dampage & Mohamed A. Mohamed, 2021. "A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5777-:d:559304
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    References listed on IDEAS

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    Cited by:

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    2. Jian Xiao & Wei Hou, 2022. "Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    3. Felipe Ramos & Aline Pinheiro & Rafaela Nascimento & Washington de Araujo Silva Junior & Mohamed A. Mohamed & Andres Annuk & Manoel H. N. Marinho, 2022. "Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids," Sustainability, MDPI, vol. 14(21), pages 1-26, October.
    4. Wei Hou & Rita Yi Man Li & Thanawan Sittihai, 2022. "Management Optimization of Electricity System with Sustainability Enhancement," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    5. Qunpeng Fan, 2022. "Management and Policy Modeling of the Market Using Artificial Intelligence," Sustainability, MDPI, vol. 14(14), pages 1-14, July.

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