Ze Yu

Hongwei Zhen, Ze Yu, Xin Xiang et al.

Power electronics systems are increasingly exposed to cyber threats due to their integration with digital controllers and communication networks. However, an attacker-oriented metric is still lacking to quantify the extent to which a node can be pushed toward instability within a privilege-constrained action space. This letter proposes an impedance-based Attack Reachable Domain (ARD) framework that maps feasible adversarial actions to critical-eigenvalue migration through impedance reshaping. Based on the ARD, an Attack Penetration Index is defined to quantify node-level cyber-vulnerability by jointly characterizing the penetration of the nominal stability margin and the accessibility of successful destabilizing attacks within a privilege-constrained action space. To make the proposed assessment computable when inverter models are unavailable, a practical gray-box workflow is further established by integrating existing impedance identification and differentiable surrogate tools. Case studies on a 4-bus system and a modified IEEE 39-bus system show that coordinated cross-layer manipulations are markedly more damaging than isolated single-layer attacks, and that the proposed metric reveals vulnerability patterns that cannot be inferred from grid-strength indicators.

Hongwei Zhen, Ze Yu, Xin Xiang et al.

The high penetration of voltage source converters in modern smart microgrids enhances operational flexibility while introducing complex cyber-physical vulnerabilities. Existing cyber-attack studies either require detailed knowledge of system topology and controller dynamics or depend on repeated online interactions, which may compromise practicality by generating operationally infeasible or limit-violating commands. This article investigates a dispatch command manipulation attack and develops an admittance-guided framework to identify the vulnerable inverter and the worst-case dispatch command that most severely degrades system stability. A compromised inverter is utilized to inject controlled harmonic perturbations for sparse admittance measurement, and a physics-informed neural network is then employed to reconstruct the operating-point-dependent admittance of target inverters over the feasible dispatch region. Based on the reconstructed admittance, a stability-margin-oriented optimization is formulated to locate the most vulnerable inverter and the corresponding worst-case dispatch command. Controller hardware-in-the-loop experiments on a five-inverter microgrid demonstrate that the identified command can drive the system into severe sub-synchronous oscillations while remaining within nominal dispatch bounds, highlighting the need for stability-aware command screening beyond static limit checking.