用多任务学习同时评估电力系统四大稳定性，准确率更高。
Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment
- 共享编码器+多解码器结构，实现四类稳定性的知识共享。
- 在IEEE 68节点系统上优于现有最先进方法，提升明显。
- 适合电力系统安全评估、机器学习应用研究者参考。
本文提出一种基于多任务学习（MTL）的新方法，将电力系统安全评估问题重构为多标签分类任务。所提MTL框架可同时评估静态、电压、暂态和小信号稳定性，相比现有最先进机器学习方法，在准确率与可解释性方面均有提升。该框架包含共享编码器和多个解码器，支持不同稳定性任务间的知识迁移。在IEEE 68节点系统上的实验表明，该方法性能显著优于现有主流方法。
原文摘要 · Abstract (English)
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.
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