66 citations · 66 across the 6 of their papers we have counts for
6 papers
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
Tong Wu, Andrew Campbell, Anna Scaglione
Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive controllers, schedulers, an…
Differentially Private Synthetic Voltage Phasor Release for Distribution Grids
Andrew Campbell, Chenyue Zhang, Anna Scaglione +3
Training machine learning models, including Grid Foundation Models (GFMs), requires large volumes of realistic grid data, yet substantial privacy concerns discourage utilities and…
Geometric Pareto Control: Physics-Supervised Pareto Representation Learning via Riemannian Energy-Gradient Flow
Tong Wu, Anna Scaglione
We study multi-objective sequential control problems in physical systems whose dynamics and operational constraints are known or can be represented by accurate physics-based models…
Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations
Tong Wu, Anna Scaglione, Sandy Miguel +1
This work addresses a fundamental challenge in applying deep learning to power systems: developing neural network models that transfer across significant system changes, including…
A Review of Safe Reinforcement Learning Methods for Modern Power Systems
Tong Su, Tong Wu, Junbo Zhao +2
Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Convent…
Differentially Private Communication of Measurement Anomalies in the Smart Grid
Nikhil Ravi, Anna Scaglione, Sean Peisert +1
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data. Our DP approach conceals c…