A Binary Decision Diagram Based Cascade Prognostics Scheme For Power Systems

Power systems face cascading failure risks where failures in one component can trigger widespread blackouts affecting millions of customers. Identifying critical contingencies that could lead to cascading outages is computationally challenging as the number of possible combinations grows exponentially. This work is innovative because it provides a practical approach using reduced-order decision diagrams to efficiently identify worst-case cascading scenarios, enabling operators to proactively strengthen system resilience against critical contingencies.

This paper presents a binary decision diagram-based cascade prognostics scheme for power systems that efficiently identifies critical component outages and their cascading effects. The approach uses reduced-order BDDs to encode blackout-causing states and transition relations, enabling fast look-up of cascading consequences. The methodology addresses the challenge of high-order contingency analysis by providing computationally efficient mechanisms to identify critical component combinations that could lead to widespread blackouts.

The BDD-based approach successfully identifies critical N-K contingencies in IEEE 14-bus and IEEE 39-bus systems, demonstrating that the method can encode complex cascading behaviors in compact data structures. Results show that the approach finds critical vulnerabilities that could cause system blackouts and evaluates load curtailment actions that can mitigate cascading failures. The methodology enables practical contingency analysis for resilience assessment in power systems.