activity
20172022
most citedHybrid QSS and Dynamic Extended-Term Simulation Based on Holomorphic Embedding

1 citations · 2 across the 3 of their papers we have counts for

collaborators

8 papers

math.DS2022

Simulation of Transients in Natural Gas Networks via A Semi-analytical Solution Approach

Xin Xu, Rui Yao, Kai Sun +1

Simulation and control of the transient flow in natural gas networks involve solving partial differential equations (PDEs). This paper proposes a semi-analytical solutions (SAS) ap…

cs.DC20211 cited

Contingency Analysis Based on Partitioned and Parallel Holomorphic Embedding

Rui Yao, Feng Qiu, Kai Sun

In the steady-state contingency analysis, the traditional Newton-Raphson method suffers from non-convergence issues when solving post-outage power flow problems, which hinders the…

cs.CE20211 cited

Hybrid QSS and Dynamic Extended-Term Simulation Based on Holomorphic Embedding

Rui Yao, Feng Qiu

Power system simulations that extend over a time period of minutes, hours, or even longer are called extended-term simulations. As power systems evolve into complex systems with in…

eess.SY2021

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling

Yichen Zhang, Hantao Cui, Jianzhe Liu +4

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained uni…

eess.SY2020

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

Dong Chen, Kaian Chen. Zhaojian Li, Tianshu Chu +3

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-base…

cs.LG2020

Deep Active Learning for Solvability Prediction in Power Systems

Yichen Zhang, Jianzhe Liu, Feng Qiu +2

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism. Machine learning methods have been proposed to approach the r…