Leveraging Multi-Task Learning for multi-label power system security assessment

Abstract

This paper introduces a novel approach to the power system security assessment using Multi-Task Learning, and reformulating the problem as a multi-label classification task. The proposed Multi-Task learning 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.