Robustness Analysis of AI Models in Critical Energy Systems

• URL: http://arxiv.org/abs/2406.14361v1
• Date: Thu, 20 Jun 2024 14:34:36 GMT
• Title: Robustness Analysis of AI Models in Critical Energy Systems
• Authors: Pantelis Dogoulis, Matthieu Jimenez, Salah Ghamizi, Maxime Cordy, Yves Le Traon,
• Abstract summary: This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the $N-1$ security criterion. Our results highlight a significant loss in accuracy following the disconnection of a line.%under this security criterion.
• Score: 17.13189303615842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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