Robustness Analysis of AI Models in Critical Energy Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911927744069632 |
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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 |