Robustness Analysis of AI Models in Critical Energy Systems

Fuente: arXiv
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Main Authors: Dogoulis, Pantelis, Jimenez, Matthieu, Ghamizi, Salah, Cordy, Maxime, Traon, Yves Le
Format: Preprint
Published: 2024
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author Dogoulis, Pantelis
Jimenez, Matthieu
Ghamizi, Salah
Cordy, Maxime
Traon, Yves Le
author_facet Dogoulis, Pantelis
Jimenez, Matthieu
Ghamizi, Salah
Cordy, Maxime
Traon, Yves Le
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2406_14361
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Robustness Analysis of AI Models in Critical Energy Systems
Dogoulis, Pantelis
Jimenez, Matthieu
Ghamizi, Salah
Cordy, Maxime
Traon, Yves Le
Artificial Intelligence
Systems and Control
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.
title Robustness Analysis of AI Models in Critical Energy Systems
topic Artificial Intelligence
Systems and Control
url https://arxiv.org/abs/2406.14361