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Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection
arXiv – CS AI|Mariam Elnour, Mohammad AlShaikh Saleh, Rachad Atat, Xiang Huo, Abdulrahman Takiddin, Muhammad Ismail, Hasan Kurban, Katherine R. Davis, Erchin Serpedin||6 views
🤖AI Summary
Researchers developed a physics-informed graph transformer network (PIGTN) for smart grid attack detection, using genetic algorithms to optimize sensor placement. The system achieved up to 37% accuracy improvement and 73% better detection rates while reducing false alarms to 0.3% across multiple power system benchmarks.
Key Takeaways
- →Novel PIGTN-based detection model outperforms existing graph network variants by incorporating AC power flow constraints.
- →Joint optimization framework using NSGA-II genetic algorithm improves both sensor placement and detection performance.
- →System demonstrates robustness under sensor failures across seven benchmark power systems from 14 to 200 bus configurations.
- →Detection accuracy improved by up to 37% with 73% better detection rates and only 0.3% false alarm rate.
- →Optimized sensor layouts reduced average state estimation error by 61-98% in power system monitoring.
#smart-grid#ai-detection#cybersecurity#graph-transformers#power-systems#sensor-optimization#attack-detection#physics-informed-ai
Read Original →via arXiv – CS AI
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