多层GNN用于电网中的预测性维护和集群
Muhammad Kazim1, Harun Pirim1, Chau Le2
1Industrial & Manufacturing Engineering, North Dakota State University, NDSU Department 2485, PO Box 6050, Fargo, ND 58108-6050, USA.
iScience
|October 16, 2025
概括
一个新的图形神经网络 (GNN) 框架通过分析变电站数据来预测停电. 这种先进的模型提高了电网可靠性,并降低了与计划外的电力中断相关的经济成本.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 网络科学 网络科学
背景情况:
- 意外的停电会造成巨大的经济损失.
- 有效地预测和管理这些中断对于电网可靠性至关重要.
- 现有的方法可能无法完全捕捉电网内的复杂的相互依存关系.
研究的目的:
- 使用多层图形神经网络 (GNN) 开发一个用于意外停电的预测框架.
- 整合空间,时间和统计协同失败模式,以提高预测准确度.
- 支持电力公用事业的预测性维护和弹性规划.
主要方法:
- 利用了347个变电站的7年运行数据.
- 开发了一种多层GNN,包含加权图形卷积和注意力机制.
- 编码的空间,短期的共同发生,和统计丰富的共同失败模式.
- 应用集群,根据事故率和恢复时间将变电站分类为风险组.
主要成果:
- 实现了0.8935的30天F1得分,用于预测需要干预的变电站.
- 成功将变电站分为八个不同的风险组.
- 确定了高风险群体,其事件率是低风险群体的六倍以上.
结论:
- 该GNN框架为中断预测和弹性规划提供了一种综合方法.
- 使公用事业公司能够优化维护计划,检查优先级和电网硬化投资.
- 提供数据驱动的策略,以最大限度地减少停电影响,提高整体电网可靠性.
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