Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment

2025-05-09 · Muhy Eddin Za'ter, Amir Sajad, Bri-Mathias Hodge

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.

📄 PDF Abstract BibTeX arXiv:2505.06207

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