16 citations · 40 across the 9 of their papers we have counts for
9 papers
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Ruisheng Diao, Di Shi, Bei Zhang +6
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presen…
AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs
Tu Lan, Jiajun Duan, Bei Zhang +4
This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constr…
A Deep Reinforcement Learning Based Approach for Optimal Active Power Dispatch
Jiajun Duan, Haifeng Li, Xiaohu Zhang +6
The stochastic and dynamic nature of renewable energy sources and power electronic devices are creating unique challenges for modern power systems. One such challenge is that the c…
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
Ruisheng Diao, Zhiwei Wang, Di Shi +3
Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response. Traditional theoretical assumption…
Probabilistic Load Forecasting via Point Forecast Feature Integration
Qicheng Chang, Yishen Wang, Xiao Lu +4
Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention f…
Adaptive Online Learning with Momentum for Contingency-based Voltage Stability Assessment
Zhijie Nie, Xiaohu Zhang, Xiaoying Zhao +4
Voltage stability refers to the ability of a power system to maintain acceptable voltages among all buses under normal operating conditions and after a disturbance. In this paper,…
Submodular Load Clustering with Robust Principal Component Analysis
Yishen Wang, Xiao Lu, Yiran Xu +4
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including…
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation
Yao Cui, Zhehan Yi, Jiajun Duan +2
This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is prop…
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties
Jiajun Duan, Zhehan Yi, Di Shi +2
In this paper, a neural-network (NN)-based online optimal control method (NN-OPT) is proposed for ultra-capacitors (UCs) energy storage system (ESS) in hybrid AC/DC microgrids invo…