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Tayebi, A., Sharifi, R., Salemi, A., Faghihi, F. (2020). Presentation of an Algorithm for Identification of the Most Vulnerable Bus in Electric Smart Grid Through Cyber-Attack Based on State Estimation. Electronic and Cyber Defense, 11(4), 391-401.
A. H. Tayebi; R. Sharifi; A. H. Salemi; F. Faghihi. "Presentation of an Algorithm for Identification of the Most Vulnerable Bus in Electric Smart Grid Through Cyber-Attack Based on State Estimation". Electronic and Cyber Defense, 11, 4, 2020, 391-401.
Tayebi, A., Sharifi, R., Salemi, A., Faghihi, F. (2020). 'Presentation of an Algorithm for Identification of the Most Vulnerable Bus in Electric Smart Grid Through Cyber-Attack Based on State Estimation', Electronic and Cyber Defense, 11(4), pp. 391-401.
Tayebi, A., Sharifi, R., Salemi, A., Faghihi, F. Presentation of an Algorithm for Identification of the Most Vulnerable Bus in Electric Smart Grid Through Cyber-Attack Based on State Estimation. Electronic and Cyber Defense, 2020; 11(4): 391-401.

Presentation of an Algorithm for Identification of the Most Vulnerable Bus in Electric Smart Grid Through Cyber-Attack Based on State Estimation

علوم و فناوریهای پدافند نوین
Volume 11, Issue 4 - Serial Number 42, January 2021, Pages 391-401    PDF (1.05 M)
Document Type: -
Authors
A. H. Tayebi1; R. Sharifi* ; A. H. Salemi1; F. Faghihi2
1Department of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, Iran
2Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Receive Date: 26 January 2020,  Revise Date: 06 May 2020,  Accept Date: 11 May 2020 
Abstract
Considering the power grids automation and network data transferring in telecommunication infrastructure, the possibility of planning a cyber-attack grows up intensively. In this regard, the optimization of the budget for attack (BA) and budget for defense (BD) in a power network through cyber-attack is so crucial. In this paper, regarding the cyber invasion phase, the choice of the most vulnerable operating bus using the state estimation technique through a new algorithm is analyzed and simulated. For this purpose, false data injection is performed on the data sent from the PMU in such a way that it is not detectable to the network dispatching operator under the invasion. As a case study implementation of the suggested algorithm for a 14 IEEE bus network is carried out and the best bus in terms of exposure under attack is identified. The performance of this algorithm is based on the results obtained from state estimating of the grid after occurring a cyber-attack on different buses. Finally, the part of the grid, which the destruction of information in that part leads to the most damage to the grid, is determined.
Keywords
False Data Injection; Undetectable Attack; Power System; State Estimation; Measurement Vector
References
[1]     Wang, Y.; Amin, M. M.; Fu, J.; Moussa, H. B. “A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids”; IEEE Access 2017, 5, 26022-26033.##

[2]     He, Y.; Mendis, G. J.; Wei, J. “Real-Time Detection of False Data Injection Attacks in Smart Grid: a Deep Learning-Based Intelligent Mechanism”; IEEE Trans. Smart Grid 2017, 8, 2505-2516.##

[3]     Asgari, A.; Firouzjah, K. G. “Optimal PMU Placement for Power System Observability Considering Network Expansion andN − 1 Contingencies”; IET Gener. Transm. Distrib. 2018, 12, 4216-4224.##

[4]     Lu, C.; Wang, Z.; Ma, M.; Shen, R.; Yu, Y. “An Optimal PMU Placement with Reliable Zero Injection Observation”; IEEE Access 2018, 6, 54417-54426.##

[5]     Guan, Y.; Ge, X. “Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks”; IEEE Trans. Signal Inf. Process. Networks 2018, 4, 48-59.##

[6]     Deng, R.; Xiao, G.; Lu, R. “Defending Against False Data Injection Attacks on Power System State Estimation,” IEEE Trans. Ind. Inf. 2017, 13, 198-207.##

[7]     Deng, R.; Xiao, G.; Lu, R.; Liang, H.; Vasilakos, A. V. “False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: a Survey”; IEEE Trans. Ind. Inf. 2017, 13, 411-423.##

[8]     Monticelli, A. “State Estimation in Electric Power Systems a Generalized Approach”; Kluwer Academic Publishers, 1999.##

[9]     Huang, Y.; Tang, J.; Cheng, Y.; Li, H.; Campbell, K. A. “Real-Time Detection of False Data Injection in Smart Grid Networks: An Adaptive CUSUM Method and Analysis”; IEEE Syst. J. 2016, 10, 532-543.##

[10]  Gaffarpour, R.; Jam, A.; Ranjbar, A. M. “Optimal Mix of Distributed Generation Allocation to Improve the Security of Energy Supply in Defensive Sites Using Principles of Passive Defence”; Advanced Defence Sci. & Tech. 2015, 7, 19-32. (In Persian).##

[11]  Bi, S.; Zhang, Y. J. “Graphical Methods for Defense against False-Data Injection Attacks on Power System State Estimation”; IEEE Trans. Smart Grid 2014, 5, 1216-1227.##

[12]  Zamani Gargari, M.; Ghaffarpour, R. “Increasing Energy Security by Using the Concept of Resiliency in Multi-Energy Infrastructures”; Advanced Defence Sci. & Tech. 2019, 10, 419-432.##

[13]  He, Y.; Mendis, G. J.; Wei, J. “Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism”; IEEE Trans. Smart Grid 2017, 8, 2505-2516.##

[14]  Mohammadpourfard, M.; Sami, A.; Weng, Y. “Identification of False Data Injection Attacks With Considering the Impact of Wind Generation and Topology Reconfigurations”; IEEE Trans. Sustainable Energy 2018, 9, 1349-1364.##

[15]  Taher, S. A.; Mahmoodi, H.; Aghaamouei, H. “Optimal PMU Location in Power Systems Using MICA”; Alex. Eng. J. 2016, 55, 399-406.##

[16]  Liang, G.; Weller, S. R.; Zhao, J.; Luo, F.; Dong, Z. Y. “A Framework for Cyber-Topology Attacks: Line-Switching and New Attack Scenarios”; IEEE Trans. Smart Grid 2017, 10, 1704-1712.##

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