6 papers
Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
Haoran Li, Lihao Mai, Muhao Guo +2
Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reliable connectivity records re…
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…
Retrieval-Guided Photovoltaic Inventory Estimation from Satellite Imagery for Distribution Grid Planning
Muhao Guo, Lihao Mai, Erik Blasch +3
The rapid expansion of distributed rooftop photovoltaic (PV) systems introduces increasing uncertainty in distribution grid planning, hosting capacity assessment, and voltage regul…
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…
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…
From Imperfect Signals to Trustworthy Structure: Confidence-Aware Inference from Heterogeneous and Reliability-Varying Utility Data
Haoran Li, Lihao Mai, Muhao Guo +4
Accurate distribution grid topology is essential for reliable modern grid operations. However, real-world utility data originates from multiple sources with varying characteristics…