Robustness Analysis of AI Models in Critical Energy Systems
It addresses the problem of AI reliability in critical energy systems for grid operators, but it is incremental as it focuses on scenario analysis without introducing new methods.
This paper analyzed the robustness of AI models for power grid operations under the N-1 security criterion, finding a significant loss in accuracy after line disconnections, though specific numbers were not provided.
This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the $N-1$ security criterion. While these models perform well in regular grid settings, our results highlight a significant loss in accuracy following the disconnection of a line.%under this security criterion. Using graph theory-based analysis, we demonstrate the impact of node connectivity on this loss. Our findings emphasize the need for practical scenario considerations in developing AI methodologies for critical infrastructure.