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

2025-05-09

AI Summary

This paper addresses the challenge of jointly modeling and interpreting multiple stability types—static, voltage, transient, and small-signal—in power system security assessment. To this end, we propose a multi-task learning (MTL)-based multi-label classification framework. Methodologically, we introduce, for the first time, a shared-encoder–multi-decoder neural architecture that unifies the four stability assessments into a single multi-label classification task, enabling both cross-task feature sharing and task-specific representation decoupling. Experimental results on the IEEE 68-bus system demonstrate that our approach achieves superior accuracy for all four stability classifications compared to state-of-the-art methods. The key contribution is establishing a novel paradigm for integrated multi-stability evaluation, delivering an end-to-end solution that simultaneously ensures high predictive accuracy and model interpretability for intelligent grid security analysis.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Learning

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Assessing power system security via multi-label classification
• Simultaneously evaluating multiple stability types using MTL
• Improving accuracy and interpretability over existing ML methods

Innovation

• Multi-Task Learning for multi-label classification
• Shared encoder with multiple decoders
• Simultaneous assessment of four stability types