用机器学习动态选停哪些负荷，防电网失稳攻击
Machine-Learning Driven Load Shedding to Mitigate Instability Attacks in Power Grids
- 基于改进普罗尼分析检测电网失稳攻击信号
- 在IEEE 14节点系统上验证可及时触发防御
- 为电网失稳攻击提供可落地的应对方案
随着社会对关键基础设施依赖加深，其复杂性也日益提升，导致新型攻击风险增加，亟需新防御策略。本文聚焦电网失稳攻击——攻击者通过引入不稳定的动态行为引发级联停电。常规缓解手段为负荷切除（load-shedding），即系统调度员选择部分负荷断电直至系统恢复稳定。然而，当前缺乏系统性方法来确定切除哪些负荷。本文提出一种数据驱动的负荷切除决策方法，在IEEE 14 Bus System上通过Achilles Heel Technologies Power Grid Analyzer进行验证，结果表明改进普罗尼分析（MPA）可有效检测失稳攻击并触发防御机制。
原文摘要 · Abstract (English)
Critical infrastructures are becoming increasingly complex as our society becomes increasingly dependent on them. This complexity opens the door to new possibilities for attacks and a need for new defense strategies. Our work focuses on instability attacks on the power grid, wherein an attacker causes cascading outages by introducing unstable dynamics into the system. When stress is place on the power grid, a standard mitigation approach is load-shedding: the system operator chooses a set of loads to shut off until the situation is resolved. While this technique is standard, there is no systematic approach to choosing which loads will stop an instability attack. This paper addresses this problem using a data-driven methodology for load shedding decisions. We show a proof of concept on the IEEE 14 Bus System using the Achilles Heel Technologies Power Grid Analyzer, and show through an implementation of modified Prony analysis (MPA) that MPA is a viable method for detecting instability attacks and triggering defense mechanisms.
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