Detection of Stealthy False Data Injection Attacks in Unobservable Distribution Networks

Rajasekaran, James K.; Natarajan, Balasubramaniam; Pahwa, Anil; Wu, Hongyu

doi:10.1109/isgt50606.2022.9817491

Detection of Stealthy False Data Injection Attacks in Unobservable Distribution Networks

Conference · Sun Apr 24 04:00:00 EDT 2022 · 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

DOI:https://doi.org/10.1109/isgt50606.2022.9817491· OSTI ID:1908546

Rajasekaran, James K. ^[1]; Natarajan, Balasubramaniam ^[2]; Pahwa, Anil ^[2]; Wu, Hongyu ^[2]

Kansas State University,Manhattan,USA; Kansas State University
Kansas State University,Manhattan,USA

In this paper, a composite scheme is proposed for detecting stealthy data manipulation attacks on distribution system which is unobservable with standard least squares based state estimators. This technique has three stages where the process of data imputation, voltage phasor estimation and the bad data detection are carried out in a systematic manner. The proposed approach is then integrated with moving target defense strategies which perturbs the network parameters to reveal stealthy false data injection attacks. The proposed approach is tested is validated on a three-phase, unbalanced 37-node distribution system and its results are presented. It is shown that the proposed approach has the ability to accurately detect the presence of FDI attacks using limited measurements (i.e., the test system is unobservable).

Research Organization:: Kansas State University

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

DOE Contract Number:: EE0008767

OSTI ID:: 1908546

Report Number(s):: DOE-KSU-8767

Conference Information:: Journal Name: 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

Country of Publication:: United States

Language:: English

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