10 citations · 17 across the 5 of their papers we have counts for
5 papers
Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks
Joe Gorka, Tim Hsu, Wenting Li +2
Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for…
Ranking-Based Physics-Informed Line Failure Detection in Power Grids
Aleksandra Burashnikova, Wenting Li, Massih Amini +2
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment…
PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels
Wenting Li, Deepjyoti Deka
Electrical faults may trigger blackouts or wildfires without timely monitoring and control strategy. Traditional solutions for locating faults in distribution systems are not real-…
Physics-Informed Learning for High Impedance Faults Detection
Wenting Li, Deepjyoti Deka
High impedance faults (HIFs) in distribution grids may cause wildfires and threaten human lives. Conventional protection relays at substations fail to detect more than 10\% HIFs si…
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
Wenting Li, Deepjyoti Deka, Michael Chertkov +1
Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniqu…