Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process

no code implementations • IEEE Transactions on Smart Grid 2022 • Di Cao, Member, Junbo Zhao, Weihao Hu, Senior Member, Qishu Liao, Qi Huang, Zhe Chen, Fellow, IEEE

Abstract—This paper addresses the distribution system state estimation (DSSE) with unknown topology change.

Bayesian Inference Transfer Learning

Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning

no code implementations • 24 Jun 2020 • Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen, Frede Blaabjerg

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice.

Decision Making

Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning

no code implementations • 31 May 2020 • Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen

This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.

Clustering