用图注意力网络加速微电网脆弱性评估，兼具高精度与可解释性。
Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids
- 融合蒙特卡洛模拟与自注意力池化图神经网络，动态识别关键节点
- 评估误差低至0.001，响应时间小于1秒，实现实时分析
- 适合电力系统安全评估、智能电网设计人员快速决策
独立微电网在孤岛和野外作战等场景中通过整合分布式能源与负荷提供电力，其对有意攻击或自然灾害的脆弱性快速准确评估对风险防范与设计优化至关重要。传统蒙特卡洛模拟（MCS）计算成本高、耗时长，现有机器学习方法则常缺乏准确性与可解释性。本文提出一种融合MCS与自注意力池化图注意力网络（GAT-S）的快速可解释脆弱性评估框架。MCS生成训练数据，GAT-S模型学习微电网的结构与电气特性，并智能评估其脆弱性。该模型通过动态分配注意力权重提升可解释性与计算效率。多组微电网配置实验表明，该框架实现极低均方误差（0.001），响应时间低于1秒，且输出可解释结果。
原文摘要 · Abstract (English)
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. To 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 (GAT-S). MCS generates training data, while the GAT-S model learns the structural and electrical characteristics of the microgrid and further assesses its vulnerability intelligently. The GAT-S improves explainability and computational efficiency by dynamically assigning attention weights to critical nodes. Comprehensive experimental evaluations across various microgrid 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.
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