Robustness Analysis of AI Models in Critical Energy Systems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 20 Jun 2024.
Builds on
7 of 24 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020Physics-Guided Deep Neural Networks for Power Flow Analysiscited by 33
- 2021Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systemscited by 21
- 2017Introducing machine learning for power system operation supportcited by 7
- 2018Fast Power system security analysis with Guided Dropoutcited by 6
- 2020Towards an AI assistant for power grid operatorscited by 6
- 2024PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systemscited by 4
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Citing papers per year: 2025: 1.
1 paper.