32 citations · 35 across the 5 of their papers we have counts for
5 papers
Residual Deep Reinforcement Learning for Inverter-based Volt-Var Control
Qiong Liu, Ye Guo, Lirong Deng +3
A residual deep reinforcement learning (RDRL) approach is proposed by integrating DRL with model-based optimization for inverter-based volt-var control in active distribution netwo…
Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks
Qiong Liu, Ye Guo, Tong Xu
Inverter-based volt-var control is studied in this paper. One key issue in DRL-based approaches is the limited measurement deployment in active distribution networks, which leads t…
Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control
Qiong Liu, Ye Guo, Lirong Deng +3
Reference-model-assisted deep reinforcement learning (DRL) for inverter-based Volt-Var Control (IB-VVC) in active distribution networks is proposed. We investigate that a large act…
Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control
Qiong Liu, Ye Guo, Lirong Deng +4
A one-step two-critic deep reinforcement learning (OSTC-DRL) approach for inverter-based volt-var control (IB-VVC) in active distribution networks is proposed in this paper. Firstl…
Adaptive Feedforward Neural Network Control with an Optimized Hidden Node Distribution
Qiong Liu, Dongyu Li, Shuzhi Sam Ge +1
Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adap…