Distributed microgrids are conventionally dependent on communication networks to achieve secondary control objectives. This dependence makes them vulnerable to stealth data integrity attacks (DIAs) where adversaries may perform manipulations via infected transmitters and repeaters to jeopardize stability. This paper presents a physics-guided, supervised Artificial Neural Network (ANN)-based framework that identifies communication-level cyberattacks in microgrids by analyzing whether incoming measurements will cause abnormal behavior of the secondary control layer. If abnormalities are detected, an iteration through possible spanning tree graph topologies that can be used to fulfill secondary control objectives is done. Then, a communication network topology that would not create secondary control abnormalities is identified and enforced for maximum stability. By altering the communication graph topology, the framework eliminates the dependence of the secondary control layer on inputs from compromised cyber devices helping it achieve resilience without instability. Several case studies are provided showcasing the robustness of the framework against False Data Injections and repeater-level Man-in-the-Middle attacks. To understand practical feasibility, robustness is also verified against larger microgrid sizes and in the presence of varying noise levels. Our findings indicate that performance can be affected when attempting scalability in the presence of noise. However, the framework operates robustly in low-noise settings.

翻译：分布式微电网传统上依赖通信网络实现二次控制目标。这种依赖性使其易受隐蔽数据完整性攻击（DIAs）的威胁，攻击者可能通过受感染的发射器和中继器执行操控以破坏系统稳定性。本文提出一种基于物理引导的监督式人工神经网络（ANN）框架，通过分析输入测量值是否会导致二次控制层异常行为来识别微电网中的通信级网络攻击。若检测到异常，系统将遍历可用于实现二次控制目标的所有可能生成树图拓扑。随后，识别并强制执行不会引发二次控制异常的通信网络拓扑，以实现最大稳定性。通过改变通信图拓扑，该框架消除了二次控制层对受损网络设备输入的依赖，从而在不失稳的前提下实现系统弹性。多个案例研究表明该框架对虚假数据注入和中继器级中间人攻击具有鲁棒性。为评估实际可行性，还在更大规模微电网和不同噪声水平下验证了其鲁棒性。研究结果表明，在存在噪声的情况下尝试扩展规模可能影响性能，但该框架在低噪声环境中仍能稳健运行。