Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918015268814848 |
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| author | Za'ter, Muhy Eddin Sajad, Amir Hodge, Bri-Mathias |
| author_facet | Za'ter, Muhy Eddin Sajad, Amir Hodge, Bri-Mathias |
| contents | This paper introduces a novel approach to the power system security assessment using Multi-Task Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment Za'ter, Muhy Eddin Sajad, Amir Hodge, Bri-Mathias Systems and Control Machine Learning This paper introduces a novel approach to the power system security assessment using Multi-Task Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches. |
| title | Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2505.06207 |