Graph Autoencoder-Based Power Attacks Detection for Resilient Electrified Transportation Systems

The interdependence of power and electrified transportation systems introduces new challenges to the reliability and resilience of charging infrastructure. With the increasing prevalence of electric vehicles (EVs), power system attacks that can lower customers charging satisfaction rates are on the...

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Veröffentlicht in:IEEE transactions on transportation electrification Jg. 10; H. 4; S. 9539 - 9553
Hauptverfasser: Fahim, Shahriar Rahman, Atat, Rachad, Kececi, Cihat, Takiddin, Abdulrahman, Ismail, Muhammad, Davis, Katherine R., Serpedin, Erchin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Cybersecurity
Detectors
Electric power grids
Electric vehicle charging
electric vehicles (EVs)
false data injection attacks (FDIAs)
graph autoencoder (GAE)
graph neural networks (GNNs)
Infrastructure
Mathematical models
Power grids
Power measurement
Power system stability
Power systems
Reactive power
Resilience
smart grids
System effectiveness
System reliability
Traffic volume
Transportation
Transportation systems
ISSN:2332-7782, 2577-4212, 2332-7782
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Zusammenfassung:The interdependence of power and electrified transportation systems introduces new challenges to the reliability and resilience of charging infrastructure. With the increasing prevalence of electric vehicles (EVs), power system attacks that can lower customers charging satisfaction rates are on the rise. The existing false data injection attack (FDIA) detection strategies are not suitable for protecting the power-dependent transportation infrastructure since: 1) these detectors are primarily optimized for power grids alone and 2) they overlook the impact of attacks on the quality-of-service of EVs and charging stations (CSs). In response to these challenges, this article aims to develop an FDIA detection strategy that takes advantage of the data correlations between power and transportation systems, ultimately enhancing the charging satisfaction rate. To achieve this goal, we propose a graph autoencoder-based FDIA detection scheme capable of extracting spatiotemporal features from both power and transportation data. The input features of power systems are active and reactive power, while those for transportation systems are the hourly traffic volume in CSs. The proposed model undergoes comprehensive training and testing on various types of FDIAs, showcasing improved generalization abilities. Simulations are conducted on the 2000-bus power grid of the state of Texas, featuring 360 active CSs. Our investigations reveal an average detection rate of 98.3%, representing a substantial improvement of 15%-25% compared to state-of-the-art detectors. This underscores the effectiveness of our proposed approach in addressing the unique challenges posed by power-dependent electrified transportation systems.
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ISSN:2332-7782
2577-4212
2332-7782
DOI:10.1109/TTE.2024.3355094