Enhancing Cyber-Security of Distributed Robust State Estimation: Identification of Data Integrity Attacks in Multi-Operator Power System

State estimation (SE) has a crucial role to play in the monitoring and control of power grids. Although currently the SE is typically done in a centralized or hierarchical manner, distributed SE will become a significant alternative to centralized and hierarchical approaches in the future smart grids. This is because the power grids will be increasingly interconnected in future smart grids and the complexity scale of an interconnection will render centralized SE computationally formidable. Performing distributed SE requires leveraging advanced communication and computation technology. Nevertheless, relying on communication networks raises its susceptibility to data integrity attacks, such as false data injection (FDI) attacks. In this paper, we demonstrate that the attacker who compromises the communication infrastructure can launch an FDI attack on distributed SE which could circumvent present robust estimators and bad data detectors. Afterwards, to effectively defense against the proposed FDI attack, two detection methods are proposed for two different modes of an interconnected power system. A detector is developed that validates the error of estimates of the state variables relative to their actual value as an index using a threshold value for different areas when the network is being run by an operator. A controlled information dissemination strategy is utilized to securely notify all areas of each other's proposed index when the network is being run by multiple operators. The proposed algorithms are validated on the IEEE 14-bus test system.

[1]  Ali Reza Seifi,et al.  A statistical unsupervised method against false data injection attacks: A visualization-based approach , 2017, Expert Syst. Appl..

[2]  Yang Weng,et al.  Ensuring cybersecurity of smart grid against data integrity attacks under concept drift , 2020, International Journal of Electrical Power & Energy Systems.

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  Mohammad Shahidehpour,et al.  Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing , 2003 .

[5]  Oliver Kosut,et al.  Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation , 2015, IEEE Transactions on Power Systems.

[6]  Yang Weng,et al.  Benchmark of machine learning algorithms on capturing future distribution network anomalies , 2019, IET Generation, Transmission & Distribution.

[7]  Fabio Pasqualetti,et al.  Centralized Versus Decentralized Detection of Attacks in Stochastic Interconnected Systems , 2019, IEEE Transactions on Automatic Control.

[8]  David K. Y. Yau,et al.  On Effectiveness of Detecting FDI Attacks on Power Grid using Moving Target Defense , 2019, 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[9]  Xiaodong Wang,et al.  Quickest Detection of False Data Injection Attack in Wide-Area Smart Grids , 2015, IEEE Transactions on Smart Grid.

[10]  György Dán,et al.  Security of Fully Distributed Power System State Estimation: Detection and Mitigation of Data Integrity Attacks , 2014, IEEE Journal on Selected Areas in Communications.

[11]  Yang Weng,et al.  Identification of False Data Injection Attacks With Considering the Impact of Wind Generation and Topology Reconfigurations , 2018, IEEE Transactions on Sustainable Energy.

[12]  Zhu Han,et al.  Detecting False Data Injection Attacks on Power Grid by Sparse Optimization , 2014, IEEE Transactions on Smart Grid.

[13]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[14]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2009, CCS.