DynaTrust-VVC：面向网络弹性多智能体无功-电压控制的基于方向物理信息信任的检测与缓解
DynaTrust-VVC: Directional Physics-Informed Trust-Based Detection and Mitigation for Cyber-Resilient Multi-Agent Volt--VAR Control
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中文总结 AI 辅助
针对分布式无功-电压控制中的虚假数据注入攻击，提出DynaTrust-VVC框架，利用方向物理信息信任和自适应阈值实现快速检测与缓解，显著提升网络弹性且不增加误报。
中文摘要 AI 辅助
分布式无功-电压控制依赖于电压测量，因此容易受到虚假数据注入的攻击。邻居佐证可以将孤立的损坏测量与物理扰动区分开来，但协调攻击的智能体可以相互虚假佐证。本文提出DynaTrust-VVC，一种网络弹性的多智能体无功-电压框架，该框架根据每个传入邻居消息的报告电压增量与基于有功和无功功率变化的灵敏度预测之间的一致性，为其分配一个方向信任值。信任在物理失配后立即下降，并在持续一致后逐渐恢复。DynaTrust-VVC进一步使用物理自适应证据阈值和基于反事实安全电压恢复的缓解措施。所提出的方法使用非线性IEEE 123节点馈线、分钟级住宅负荷曲线和27个匹配的重复实验进行了验证。DynaTrust-VVC在所有27个匹配的重复实验中，在攻击开始后一分钟内识别出协调的三节点攻击，而具有固定邻居信任的相同配置则无法识别任何攻击。综合弹性指数也从0.227增加到0.315，而良性误报率没有统计学显著增加。
英文摘要
Distributed Volt--VAR control relies on voltage measurements and is therefore vulnerable to false-data injection. Neighbor corroboration can distinguish an isolated corrupted measurement from a physical disturbance, but coordinated agents can falsely corroborate one another. This paper proposes DynaTrust-VVC, a cyber-resilient multi-agent Volt--VAR framework that assigns each incoming neighbor message a directional trust value based on the consistency between its reported voltage increment and a sensitivity-based prediction from active- and reactive-power changes. Trust falls immediately after a physics mismatch and recovers gradually after sustained consistency. DynaTrust-VVC further uses a physics-adaptive evidence threshold and counterfactual safe-voltage recovery-based mitigation. The proposed method was validated using a nonlinear IEEE 123-bus feeder with minute-resolution residential profiles and 27 matched replicates. DynaTrust-VVC identifies a coordinated three-bus attack one minute after onset in all 27 matched replicates, whereas an otherwise identical configuration with fixed neighbor trust identifies none. The composite resilience index also increases from 0.227 to 0.315, with no statistically significant increase in the benign false-alarm rate.
发表机构
- West Virginia University（西弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
