11 papers
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
Chenhan Xiao, Xinyu He, Haoran Li +2
Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation…
Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks
Xin Li, Chenhan Xiao, Jonathan Cohen +3
False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when the injected signals align w…
Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems
Xin Li, Chenhan Xiao, Jonathan Cohen +3
The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated d…
Predicting Power-System Dynamic Trajectories with Foundation Models
Haoran Li, Lihao Mai, Chenhan Xiao +2
As power systems transition toward renewable-rich and inverter-dominated operations, accurate time-domain dynamic analysis becomes increasingly critical. Such analysis supports key…
Owen-based Semantics and Hierarchy-Aware Explanation (O-Shap)
Xiangyu Zhou, Chenhan Xiao, Yang Weng
Shapley value-based methods have become foundational in explainable artificial intelligence (XAI), offering theoretically grounded feature attributions through cooperative game the…
Limits of Residual-Based Detection for Physically Consistent False Data Injection
Chenhan Xiao, Yang Weng
False data injection attacks (FDIAs) pose a persistent challenge to AC power system state estimation. In current practice, detection relies primarily on topology-aware residual-bas…