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Distributed Mitigation Layers for Voltages and Currents Cyber-Attacks on DC Microgrids Interfacing Converters

Author

Listed:
  • Ahmed H. EL-Ebiary

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Mohamed Mokhtar

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Atef M. Mansour

    (Power Electronics and Energy Conversion Department, Electronics Research Institute, Cairo 12622, Egypt)

  • Fathy H. Awad

    (Power Electronics and Energy Conversion Department, Electronics Research Institute, Cairo 12622, Egypt)

  • Mostafa I. Marei

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Mahmoud A. Attia

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

Abstract

The wide use of communication layers in DC microgrids to transmit voltage and current measurements of each distributed generator unit (DGU) increases the possibility of exposure to cyber-attacks. Cyber-attackers can manipulate the measured data to distort the control system of microgrids, which may lead to a shutdown. This paper proposes distributed mitigation layers for the false data injection attacks (FDIA) on voltages and currents of DGUs in meshed DC microgrids. The proposed control strategy is based on integrating two layers for cyber-attack detection and mitigation to immune the primary and the secondary control loops of each DGU. The first layer is assigned to mitigate FDIAs on the voltage measurements needed for the voltage regulation task of the primary control loop. The second layer is devoted to the mitigation of FDIAs on the DGU current measurements, which are crucial for the secondary control level to guarantee the proper current sharing of each DGU. Artificial neural networks (ANNs) are employed to support these layers by estimating the authenticated measurements. Different simulation and experimental case studies are provided to demonstrate the proposed mitigation layers’ effectiveness in detecting and mitigating cyber-attacks on voltage and current measurements. The simulation and experimental results are provided to evaluate the dynamic performance of the suggested control approach and to ensure the accurate operation of DC microgrids despite the existence of cyber-attacks on the measurements employed in the control strategy. Moreover, the control strategy succeeds to keep the maximum voltage error and the maximum error in current sharing within tolerance.

Suggested Citation

  • Ahmed H. EL-Ebiary & Mohamed Mokhtar & Atef M. Mansour & Fathy H. Awad & Mostafa I. Marei & Mahmoud A. Attia, 2022. "Distributed Mitigation Layers for Voltages and Currents Cyber-Attacks on DC Microgrids Interfacing Converters," Energies, MDPI, vol. 15(24), pages 1-32, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9426-:d:1001818
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    References listed on IDEAS

    as
    1. Luigi Fortuna & Arturo Buscarino, 2022. "Nonlinear Technologies in Advanced Power Systems: Analysis and Control," Energies, MDPI, vol. 15(14), pages 1-4, July.
    2. Marvin Lema & Wilson Pavon & Leony Ortiz & Ama Baduba Asiedu-Asante & Silvio Simani, 2022. "Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability," Energies, MDPI, vol. 15(15), pages 1-15, July.
    3. Liang Ma & Gang Xu, 2020. "Distributed Resilient Voltage and Reactive Power Control for Islanded Microgrids under False Data Injection Attacks," Energies, MDPI, vol. 13(15), pages 1-27, July.
    4. Hoon Lee & Jin-Wook Kang & Bong-Yeon Choi & Kyung-Min Kang & Mi-Na Kim & Chang-Gyun An & Junsin Yi & Chung-Yuen Won, 2021. "Energy Management System of DC Microgrid in Grid-Connected and Stand-Alone Modes: Control, Operation and Experimental Validation," Energies, MDPI, vol. 14(3), pages 1-26, January.
    5. Ahmed H. EL-Ebiary & Mahmoud A. Attia & Mostafa I. Marei & Mariam A. Sameh, 2022. "An Integrated Seamless Control Strategy for Distributed Generators Based on a Deep Learning Artificial Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    6. Anuoluwapo Aluko & Andrew Swanson & Leigh Jarvis & David Dorrell, 2022. "Modeling and Stability Analysis of Distributed Secondary Control Scheme for Stand-Alone DC Microgrid Applications," Energies, MDPI, vol. 15(15), pages 1-18, July.
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