38 citations · 103 across the 8 of their papers we have counts for
8 papers
Stability Constrained Voltage Control in Distribution Grids with Arbitrary Communication Infrastructure
Zhenyi Yuan, Jie Feng, Yuanyuan Shi +1
We consider the problem of designing learning-based reactive power controllers that perform voltage regulation in distribution grids while ensuring closed-loop system stability. In…
Data-Enabled Predictive Control for Nonlinear Systems Based on a Koopman Bilinear Realization
Zuxun Xiong, Zhenyi Yuan, Keyan Miao +3
This paper extends the Willems' Fundamental Lemma to nonlinear control-affine systems using the Koopman bilinear realization. This enables us to bypass the Extended Dynamic Mode De…
Unsupervised Learning for Equitable DER Control
Zhenyi Yuan, Guido Cavraro, Ahmed S. Zamzam +1
In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsu…
Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers
Zhenyi Yuan, Guido Cavraro, Jorge Cortés
We consider the problem of learning local Volt/Var controllers in distribution grids (DGs). Our approach starts from learning separable surrogates that take both local voltages and…
Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation
Zhenyi Yuan, Guido Cavraro, Manish K. Singh +1
This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution networks (DNs). Aiming to…
Learning Local Volt/Var Controllers Towards Efficient Network Operation with Stability Guarantees
Guido Cavraro, Zhenyi Yuan, Manish K. Singh +1
This paper considers the problem of voltage regulation in distribution networks. The primary motivation is to keep voltages within preassigned operating limits by commanding the re…
Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees
Zhenyi Yuan, Changhong Zhao, Jorge Cortes
This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees. Building on Lyapunov stab…
Data-Driven Optimal Control of Bilinear Systems
Zhenyi Yuan, Jorge Cortes
This paper develops a method to learn optimal controls from data for bilinear systems without a priori knowledge of the system dynamics. Given an unknown bilinear system, we first…