3 citations · 3 across the 11 of their papers we have counts for
13 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…
Scalable and Reliable State-Aware Inference of High-Impact N-k Contingencies
Lihao Mai, Chenhan Xiao, Yang Weng
Increasing penetration of inverter-based resources, flexible loads, and rapidly changing operating conditions make higher-order contingency assessment increasingly import…
Dynamic Load Model for Data Centers with Pattern-Consistent Calibration
Siyu Lu, Chenhan Xiao, Yang Weng
The rapid growth of data centers has made large electronic load (LEL) modeling increasingly important for power system analysis. Such loads are characterized by fast workload-drive…
LASS-ODE: Scaling ODE Computations to Connect Foundation Models with Dynamical Physical Systems
Haoran Li, Chenhan Xiao, Lihao Mai +2
Foundation models have transformed language, vision, and time series data analysis, yet progress on dynamic predictions for physical systems remains limited. Given the complexity o…
Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning
Haoran Li, Chenhan Xiao, Muhao Guo +1
Learning dynamics is essential for model-based control and Reinforcement Learning in engineering systems, such as robotics and power systems. However, limited system measurements,…
PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis
Xinyu He, Chenhan Xiao, Haoran Li +5
Modern power systems are becoming increasingly dynamic, with changing topologies and time-varying loads driven by renewable energy variability, electric vehicle adoption, and activ…
Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee
Chenhan Xiao, Yizheng Liao, Yang Weng
Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only…
Privacy-Preserving Line Outage Detection in Distribution Grids: An Efficient Approach with Uncompromised Performance
Chenhan Xiao, Yizheng Liao, Yang Weng
Recent advancements in research have shown the efficacy of employing sensor measurements, such as voltage and power data, in identifying line outages within distribution grids. How…