A Novel False Measurement Data Detection Mechanism for Smart Grids

Muhammad Awais Shahid, Fiaz Ahmad (), Rehan Nawaz, Saad Ullah Khan, Abdul Wadood () and Hani Albalawi ()
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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

Energies, 2023, vol. 16, issue 18, 1-17

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.

Keywords: cyber-physical systems; power system state estimation; false data injection attacks; false data detection; cyber security (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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