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Electrical Engineering and Systems Science > Systems and Control

arXiv:2509.00528 (eess)
[Submitted on 30 Aug 2025 ]

Title: Game Theoretic Resilience Recommendation Framework for CyberPhysical Microgrids Using Hypergraph MetaLearning

Title: 基于超图元学习的网络物理微电网博弈论弹性推荐框架

Authors:S Krishna Niketh, Prasanta K Panigrahi, V Vignesh, Mayukha Pal
Abstract: This paper presents a physics-aware cyberphysical resilience framework for radial microgrids under coordinated cyberattacks. The proposed approach models the attacker through a hypergraph neural network (HGNN) enhanced with model agnostic metalearning (MAML) to rapidly adapt to evolving defense strategies and predict high-impact contingencies. The defender is modeled via a bi-level Stackelberg game, where the upper level selects optimal tie-line switching and distributed energy resource (DER) dispatch using an Alternating Direction Method of Multipliers (ADMM) coordinator embedded within the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The framework simultaneously optimizes load served, operational cost, and voltage stability, ensuring all post-defense states satisfy network physics constraints. The methodology is first validated on the IEEE 69-bus distribution test system with 12 DERs, 8 critical loads, and 5 tie-lines, and then extended to higher bus systems including the IEEE 123-bus feeder and a synthetic 300-bus distribution system. Results show that the proposed defense strategy restores nearly full service for 90% of top-ranked attacks, mitigates voltage violations, and identifies Feeder 2 as the principal vulnerability corridor. Actionable operating rules are derived, recommending pre-arming of specific tie-lines to enhance resilience, while higher bus system studies confirm scalability of the framework on the IEEE 123-bus and 300-bus systems.
Abstract: 本文提出了一种面向径向微电网在协同网络攻击下的物理感知网络物理弹性框架。所提出的方法通过增强模型无关元学习(MAML)的超图神经网络(HGNN)对攻击者进行建模,以快速适应不断变化的防御策略并预测高影响事件。防御者通过双层斯塔克尔伯格博弈进行建模,其中上层使用嵌入非支配排序遗传算法II(NSGA-II)中的交替方向乘子法(ADMM)协调器选择最优的联络线切换和分布式能源(DER)调度。该框架同时优化供电负荷、运行成本和电压稳定性,确保所有防御后状态满足网络物理约束。该方法首先在包含12个DER、8个关键负荷和5条联络线的IEEE 69节点配电测试系统上进行了验证,然后扩展到更高节点系统,包括IEEE 123节点馈线和一个合成的300节点配电系统。结果表明,所提出的防御策略能够恢复排名前90%的攻击的几乎全部服务,缓解电压越限,并识别出馈线2为主要的脆弱通道。得出了可操作的运行规则,建议预先准备特定的联络线以提高弹性,而更高节点系统的研究表明该框架在IEEE 123节点和300节点系统上的可扩展性。
Subjects: Systems and Control (eess.SY) ; Machine Learning (cs.LG)
Cite as: arXiv:2509.00528 [eess.SY]
  (or arXiv:2509.00528v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2509.00528
arXiv-issued DOI via DataCite

Submission history

From: Dr. Mayukha Pal [view email]
[v1] Sat, 30 Aug 2025 15:20:11 UTC (536 KB)
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