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.
@article{arxiv.2505.06207,
title = {Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment},
author = {Muhy Eddin Za'ter and Amir Sajad and Bri-Mathias Hodge},
journal= {arXiv preprint arXiv:2505.06207},
year = {2025}
}