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Thesis
Unobservable Tensor-Completion Enabled False Data Injection Attacks Against PMU State Estimation
Washington State University
Master of Science (MS), Washington State University
05/2025
DOI:
https://doi.org/10.7273/000007359
Abstract
The electric power network is continuously monitored through a comprehensive network of measuring devices to ensure its secure and efficient operation. Remote Terminal Units (RTUs) collect data on power flows and injections, while Phasor Measurement Units (PMUs) deliver highly accurate time-synchronized measurements of bus voltages and branch currents in phasor form. State Estimation (SE) determines the nodal voltage using the measurements from these devices. However, this process is vulnerable to cyberattacks, particularly False Data Injection (FDI) attacks, wherein the legitimate measurements are replaced with counterfeits to disrupt economic power dispatch and grid operations. Traditional FDI attacks targeting the PMU state estimation have been shown to bypass the conventional Chi-square bad data detectors (BDD). However, recent low-rank-based, data-driven detection methods have demonstrated effectiveness in identifying such attacks by exploiting the low-rank property of the historical PMU data. This thesis proposes a new convex tensor completion-based FDI attack (TC-FDI), designed to compromise PMU state estimation from the adversary’s standpoint. This approach manipulates measurements in alignment with historical data, aiming to remain undetected by the low-rank-based detection method. Furthermore, the proposed attack is designed to maximize voltage deviations to induce significant disruption in system operations while employing the power flow model as a constraint to preserve stealth against traditional Chi-square BDD methods. The proposed TC-FDI attack is validated on the IEEE 14-bus system, demonstrating its effectiveness to compromise PMU state estimation while remaining stealthy.
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Details
- Title
- Unobservable Tensor-Completion Enabled False Data Injection Attacks Against PMU State Estimation
- Creators
- Akash Debnath
- Contributors
- Bo Liu (Chair)Mohammad Osman (Committee Member)Scott Hudson (Committee Member)David Lowry (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- School of Engineering and Applied Sciences (TRIC)
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 68
- Identifiers
- 99901220469401842
- Language
- English
- Resource Type
- Thesis