Scalable and Reliable State-Aware Inference of High-Impact N-k Contingencies

TL;DR

This paper introduces a scalable, state-aware framework for identifying high-impact N-k contingencies in power systems, using advanced machine learning models to efficiently focus on critical outage scenarios without exhaustive enumeration.

Contribution

It presents a novel contingency inference method combining a conditional diffusion model and a topology-aware graph neural network to efficiently target high-severity outages based on current system states.

Findings

Outperforms uniform sampling in identifying severe contingencies.

Reduces computational effort while maintaining high-risk scenario detection.

Provides controllable guarantees for critical contingency coverage.

Abstract

Increasing penetration of inverter-based resources, flexible loads, and rapidly changing operating conditions make higher-order $N\!-\!k$ contingency assessment increasingly important but computationally prohibitive. Exhaustive evaluation of all outage combinations using AC power-flow or ACOPF is infeasible in routine operation. This fact forces operators to rely on heuristic screening methods whose ability to consistently retain all critical contingencies is not formally established. This paper proposes a scalable, state-aware contingency inference framework designed to directly generate high-impact $N\!-\!k$ outage scenarios without enumerating the combinatorial contingency space. The framework employs a conditional diffusion model to produce candidate contingencies tailored to the current operating state, while a topology-aware graph neural network trained only on base and $N\!-\!1$ cases efficiently constructs high-risk training samples offline. Finally, the framework is developed to provide controllable coverage guarantees for severe contingencies, allowing operators to explicitly manage the risk of missing critical events under limited AC power-flow evaluation budgets. Experiments on IEEE benchmark systems show that, for a given evaluation budget, the proposed approach consistently evaluates higher-severity contingencies than uniform sampling. This allows critical outages to be identified more reliably with reduced computational effort.