11 citations · 21 across the 12 of their papers we have counts for
4 papers · 1 filter
Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
Young-ho Cho, Shaohui Liu, Duehee Lee +1
Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid. We develop a graph convolutional generative a…
Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Shaohui Liu, Hao Zhu, Vassilis Kekatos
Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. This paper puts forth a comprehensive framework for inferring the dyna…
Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions
Shaohui Liu, Chengyang Wu, Hao Zhu
Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topolo…
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo
Wang Zhao, Shaohui Liu, Yi Wei +2
In this paper, we introduce a deep multi-view stereo (MVS) system that jointly predicts depths, surface normals and per-view confidence maps. The key to our approach is a novel sol…