IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6614-d1239669.html
   My bibliography  Save this article

A Novel False Measurement Data Detection Mechanism for Smart Grids

Author

Listed:
  • Muhammad Awais Shahid

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Fiaz Ahmad

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Rehan Nawaz

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Saad Ullah Khan

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Abdul Wadood

    (Department of Electrical Engineering, Air University Islamabad, Kamra Campus, Kamra 43570, Pakistan)

  • Hani Albalawi

    (Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia
    Renewable Energy and Energy Efficiency Center (REEEC), University of Tabuk, Tabuk 71491, Saudi Arabia)

Abstract

With the growing cyber-infrastructure of smart grids, the threat of cyber-attacks has intensified, posing an increased risk of compromised communication links. Of particular concern is the false data injection (FDI) attack, which has emerged as a highly dangerous cyber-attack targeting smart grids. This paper addresses the limitations of the variable dummy value model proposed in the authors previous work and presents a novel defense methodology called the nonlinear function-based variable dummy value model for the AC power flow network. The proposed model is evaluated using the IEEE 14-bus test system, demonstrating its effectiveness in detecting FDI attacks. It has been shown that previous detection techniques are unable to detect FDI attacks, whereas the proposed method is shown to be successful in the detection of such attacks, guaranteeing the security of the smart grid’s measurement infrastructure.

Suggested Citation

  • Muhammad Awais Shahid & Fiaz Ahmad & Rehan Nawaz & Saad Ullah Khan & Abdul Wadood & Hani Albalawi, 2023. "A Novel False Measurement Data Detection Mechanism for Smart Grids," Energies, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6614-:d:1239669
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6614/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/18/6614/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Awais Shahid & Fiaz Ahmad & Fahad R. Albogamy & Ghulam Hafeez & Zahid Ullah, 2022. "Detection and Prevention of False Data Injection Attacks in the Measurement Infrastructure of Smart Grids," Sustainability, MDPI, vol. 14(11), pages 1-25, May.
    2. Ajit Kumar & Neetesh Saxena & Souhwan Jung & Bong Jun Choi, 2021. "Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling," Energies, MDPI, vol. 15(1), pages 1-22, December.
    3. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    4. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    5. Li, Xueping & Wang, Yaokun & Lu, Zhigang, 2023. "Graph-based detection for false data injection attacks in power grid," Energy, Elsevier, vol. 263(PC).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omar A. Beg & Asad Ali Khan & Waqas Ur Rehman & Ali Hassan, 2023. "A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids," Energies, MDPI, vol. 16(22), pages 1-23, November.
    2. Zheng Wan & Hui Li, 2023. "Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples," Energies, MDPI, vol. 16(10), pages 1-22, May.
    3. Fangzong Wang & Zuhaib Nishtar, 2024. "Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach," Energies, MDPI, vol. 17(11), pages 1-24, May.
    4. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    5. Tulin Guzel & Hakan Cinar & Mehmet Nabi Cenet & Kamil Doruk Oguz & Ahmet Yucekaya & Mustafa Hekimoglu, 2023. "A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 179-193, September.
    6. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Ghulam Moeen Uddin & Waqar Muhammad Ashraf & Karolina Grabowska & Anna Zylka & Anna Kulakowska & Wojciech Nowak, 2023. "Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives," Energies, MDPI, vol. 16(8), pages 1-12, April.
    7. Guoqing Zhang & Wengen Gao & Yunfei Li & Xinxin Guo & Pengfei Hu & Jiaming Zhu, 2023. "Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter," Energies, MDPI, vol. 16(20), pages 1-20, October.
    8. Marco Bindi & Maria Cristina Piccirilli & Antonio Luchetta & Francesco Grasso, 2023. "A Comprehensive Review of Fault Diagnosis and Prognosis Techniques in High Voltage and Medium Voltage Electrical Power Lines," Energies, MDPI, vol. 16(21), pages 1-37, October.
    9. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    10. Murilo Eduardo Casteroba Bento, 2024. "Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters," Energies, MDPI, vol. 17(7), pages 1-20, March.
    11. Nakkeeran Murugesan & Anantha Narayanan Velu & Bagavathi Sivakumar Palaniappan & Balamurugan Sukumar & Md. Jahangir Hossain, 2024. "Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid," Energies, MDPI, vol. 17(8), pages 1-34, April.
    12. Bianca Magalhães & Pedro Bento & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2024. "Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection," Energies, MDPI, vol. 17(8), pages 1-21, April.
    13. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6614-:d:1239669. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.