Physics-Guided Deep Learning for Time-Series State Estimation Against False Data Injection Attacks
- University of central Florida
The modern power grid is a cyber-physical system. While the grid is becoming more intelligent with emerging sensing and communication techniques, new vulnerabilities are introduced and cyber security becomes a major concern. One type of cyber-attacks - False Data Injection Attacks (FDIAs) - exploits the limitations in traditional power system state estimation, and modifies system states without being detected. In this paper, we propose a physics-guided deep learning (PGDL) approach to defend against FDIAs. The PGDL takes real-time measurements as inputs to neural networks, outputs the estimated states, and reconstructs measurements considering power system physics. A deep recurrent neural network - Long Short-Term Memory (LSTM) - is employed to learn the temporal correlations among states. This hybrid learning model leads to a time-series state estimation method to defend against FDIAs. The simulation results using IEEE 14-bus test system demonstrate the accuracy and robustness of the proposed time-series state estimation under FDIAs.
- Research Organization:
- University of central Florida
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- EE0007998
- OSTI ID:
- 1826288
- Journal Information:
- 2019 North American Power Symposium (NAPS), Journal Name: 2019 North American Power Symposium (NAPS)
- Country of Publication:
- United States
- Language:
- English
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