False Data Injection Attacks Against State Estimation in Multiphase and Unbalanced Smart Distribution Systems

Zhuang, Peng; Deng, Ruilong; Liang, Hao

doi:10.1109/tsg.2019.2895306

Cited by 74 publications

(16 citation statements)

References 47 publications

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“…The attacker needs the information of system topology and access to measurement to compromise large number of state variables in unbalanced and multi-phase system for successful attack. With available information, the attacker develops the linear state estimator for attack construction [146]. The large amount of data is fetched through smart metres and synchro-phasors.…”

Section: Fdi In Distribution Systemmentioning

confidence: 99%

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

Bhattar

Pindoriya

Sharma

2021

IET cyber-phys. syst.

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The penetration of renewable energy sources (RES) in distribution systems has entailed heavy deployment of monitoring and control infrastructure in the distribution system. This deployment of sensors and communication infrastructure has led to the emergence of a modern grid with a complex cyber-physical network. On one hand, the cyber-physical system can play a pivotal role in coordination and control of distribution systems through distribution system state estimation (DSSE). On the other hand, the risk caused by cyber-attack has emerged as a major challenge for grid operation. The modelling aspects, namely DC loads, plug-in-vehicle and distributed generators (DGs) in DSSE and security aspects in cyber-physical distribution systems, are highlighted herein. This paper is divided into different sections emphasising on the problems and challenges associated with DSSE, cyber-attack focussing primarily on impact of false data injection (FDI) attack and co-simulation platform for investigation of vulnerability of cyber-physical systems. A comparative study among various DSSE and challenges namely, pseudo-measurement, time synchronisation and communication issues are discussed. The potential investigation of FDI and need for a co-simulation platform in distribution systems for risk analysis are also addressed. The opportunities and future research in the field of cyber-physical distribution systems are discussed in depth. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Fdi In Distribution Systemmentioning

confidence: 99%

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

Bhattar

Pindoriya

Sharma

2021

IET cyber-phys. syst.

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“…Yet the small values of the attack vector will not change the observation matrix significantly, thus it will be closely stealthy (or hard to detect) in the updated system. To build an absolutely stealthy FDI is thus defined as an iterative problem that tries a stealthy attack k at each time and updates the H k matrix, and build the new FDI k+1 to be stealthy to the updated system k+1 , until a convergence criterion is met [6] and [35]. To do so, the attacker requires all the system information exactly the same as the operator (e.g.…”

Section: A Stealthy Fdimentioning

confidence: 99%

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Langeroudi

Abdelaziz

2021

IEEE Access

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In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed. INDEX TERMS Distribution system online monitoring, false data injection, Kalman filter, state estimation NOMENCLATURE Acronyms CBF Constant bias filter DS Distribution system FDI False data injection KF Kalman filter mFDBF Modified failure-detector in bias filter PDF Probability distribution function rBDD Residual-based bad data detector SCADA Supervisory control and data acquisition SE State estimation WLS Weighted least squares Indices a, b, c Three phases (superscript) i, j, s DS node index (subscript) k Discrete time index (subscript) T Matrix transpose (superscript) Sets φ Set of nodes with power measurement ψ Set of nodes with voltage measurement Variables , Voltage magnitude and angle at node i phase p , Active and reactive power at node i phase p b Bias vector z Measurement vector x State vector ̂ State estimates at time k r Residual vector ω Measurement noise Functions and matrices h(x) Measurement function Hk Measurement function Jacobian G Estimation gain matrix R, Q Measurements and states covariance matrix

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“…It should be highlighted that for the state estimation and the associated FDI attack in the smart microgrid, most researches are addressing power transmission system approaches. A few works on the state estimation and FDI attack in MV power distribution systems, such as [90,91], are more applicable for the smart microgrids.…”

Section: Cyber-attacks On State Estimationmentioning

confidence: 99%

Cyber-Security of Smart Microgrids: A Survey

Nejabatkhah¹,

Liang

et al. 2020

Energies

Self Cite

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In this paper, the cyber-security of smart microgrids is thoroughly discussed. In smart grids, the cyber system and physical process are tightly coupled. Due to the cyber system’s vulnerabilities, any cyber incidents can have economic and physical impacts on their operations. In power electronics-intensive smart microgrids, cyber-attacks can have much more harmful and devastating effects on their operation and stability due to low inertia, especially in islanded operation. In this paper, the cyber–physical systems in smart microgrids are briefly studied. Then, the cyber-attacks on data availability, integrity, and confidentiality are discussed. Since a false data injection (FDI) attack that compromises the data integrity in the cyber/communication network is one of the most challenging threats for smart microgrids, it is investigated in detail in this paper. Such FDI attacks can target state estimation, voltage and frequency control, and smart microgrids’ protection systems. The economic and physical/technical impacts of the FDI attacks on smart microgrids are also reviewed in this paper. The defensive strategies against FDI attacks are classified into protection strategies, in which selected meter measurements are protected, and detection/mitigation strategies, based on either static or dynamic detection. In this paper, implementation examples of FDI attacks’ construction and detection/mitigation in smart microgrids are provided. Samples of recent cyber-security projects in the world, and critical cyber-security standards of smart grids, are presented. Finally, future trends of cyber-security in smart microgrids are discussed.

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False Data Injection Attacks Against State Estimation in Multiphase and Unbalanced Smart Distribution Systems

Cited by 74 publications

References 47 publications

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Cyber-Security of Smart Microgrids: A Survey

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