no code implementations • 20 May 2023 • Wenqi Cui, Guanya Shi, Yuanyuan Shi, Baosen Zhang
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations.
no code implementations • 15 May 2023 • Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés
Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder their applicability to practical systems.
no code implementations • 20 Mar 2023 • Jie Feng, Wenqi Cui, Jorge Cortés, Yuanyuan Shi
Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their deployment in real-world scenarios.
1 code implementation • 30 Nov 2022 • Wenqi Cui, Linbin Huang, Weiwei Yang, Baosen Zhang
Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data.
1 code implementation • 1 Jun 2022 • Wenqi Cui, Yan Jiang, Baosen Zhang, Yuanyuan Shi
We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.
no code implementations • 1 May 2022 • Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés
Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch.
no code implementations • 9 Mar 2022 • Wenqi Cui, Baosen Zhang
Because of the intermittent nature of these resources, the stability of distribution systems under large disturbances and time-varying conditions is becoming a key issue in practical operations.
1 code implementation • 1 Nov 2021 • Wenqi Cui, Weiwei Yang, Baosen Zhang
System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies.
no code implementations • 3 Oct 2021 • Wenqi Cui, Jiayi Li, Baosen Zhang
We explicitly engineer the structure of neural network controllers such that they satisfy the Lipschitz constraints by design.
1 code implementation • 21 Jul 2021 • Christina Doty, Shaun Gallagher, Wenqi Cui, Wenya Chen, Shweta Bhushan, Marjolein Oostrom, Sarah Akers, Steven R. Spurgeon
The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis.
1 code implementation • 5 Mar 2021 • Wenqi Cui, Baosen Zhang
The learned neural Lyapunov function is then utilized as a regularization to train the neural network controller by penalizing actions that violate the Lyapunov conditions.
1 code implementation • 11 Sep 2020 • Wenqi Cui, Yan Jiang, Baosen Zhang
As more inverter-connected renewable resources are integrated into the grid, frequency stability may degrade because of the reduction in mechanical inertia and damping.