Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids

--Independent microgrids are crucial for supplying electricity by combining distributed energy resources and loads in scenarios like isolated islands and field combat. Fast and accurate assessments of microgrid vulnerability against intentional attacks or natural disasters are essential for effective risk prevention and design optimization. However, conventional Monte Carlo simulation (MCS) methods are computationally expensive and time-consuming, while existing machine learning-based approaches often lack accuracy and explainability. T o address these challenges, this study proposes a fast and explainable vulnerability assessment framework that integrates MCS with a graph attention network enhanced by self-attention pooling (GA T -S). MCS generates training data, while the GA T - S model learns the structural and electrical characteristics of the microgrid and further assesses its vulnerability intelligently. The GA T -S improves explainability and computational efficiency by dynamically assigning attention weights to critical nodes. Comprehensive experimental evaluations across various micro-grid configurations demonstrate that the proposed framework provides accurate vulnerability assessments, achieving a mean squared error as low as 0.001, real-time responsiveness within 1 second, and delivering explainable results. An independent microgrid, like a battlefield or island mi-crogrid, operates separately from the main grid, supplying electricity to a localized area by integrating distributed energy resources and loads via interconnected buses, transformers, and lines. Assessing the vulnerability of independent micro-grids is essential to ensure its normal power supply capacity against disruptions, particularly in scenarios like deliberate attacks and natural disasters. Chenhui Lin is with the State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China.