ML Surrogate Ranks Criticality in Power-Communication Networks
Key takeaways
- Machine learning can significantly accelerate the assessment of critical component vulnerabilities in interdependent networks.
- A Gradient Boosting surrogate model achieved high accuracy in predicting contingency severity and ranking component criticality.
- The model's advantage stems from its ability to process inter-layer dependency information effectively.
- This approach enables a more efficient two-stage workflow for resilience planning, combining rapid ML assessment with targeted high-fidelity simulation.
Who benefits
Summary
This paper develops a machine learning model to rapidly predict contingency severity and rank component criticality in interdependent power-communication networks, overcoming the computational cost of high-fidelity simulations. The Gradient Boosting surrogate achieves high correlation with ground-truth simulations, enabling efficient resilience planning.
Why it matters
Professionals in critical infrastructure management can use this ML surrogate to significantly speed up resilience planning and identify vulnerabilities in complex power and communication systems. This allows for more proactive and cost-effective hardening strategies against cascading failures.
How to implement this in your domain
- 1Integrate the ML surrogate model into existing infrastructure planning software to automate initial vulnerability assessments.
- 2Prioritize hardening efforts on components identified as highly critical by the surrogate to maximize resilience impact.
- 3Develop a two-stage workflow where the ML model provides rapid initial rankings, followed by high-fidelity simulations for selected critical scenarios.
- 4Collect and preprocess relevant structural and interdependency data from your power and communication networks to train and validate similar surrogate models.
Original post by Sohini Roy, Xheni Hylviu
"arXiv:2607.08918v1 Announce Type: new Abstract: Cyber-physical power systems are vulnerable to cascading failures caused by tight interdependencies between power and communication infrastructures. Evaluating these failures over large N-k contingency sets with a high-fidelity simu…"
View on XOriginally posted by Sohini Roy, Xheni Hylviu on X · view source
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