Results 261 to 270 of about 2,418,705 (282)

Cloud-Edge Collaboration-Based Local Voltage Control for DGs With Privacy Preservation

IEEE Transactions on Industrial Informatics, 2023
The increased distributed generators (DGs) have exacerbated voltage violations in active distribution networks (ADNs). Local reactive power control of DG inverters can realize a fast response to frequent voltage fluctuations. However, commonly used model-
Jinli Zhao, Ziqi Zhang, Hao Yu, H. Ji, Peng Li, W. Xi, Jinyue Yan, Chengshan Wang   +7 more
semanticscholar   +1 more source

Graph Convolutional Network-Based Interpretable Machine Learning Scheme in Smart Grids

IEEE Transactions on Automation Science and Engineering, 2023
Smart grid is a typical application of industrial cyber-physical systems (ICPS) in the electric power industry. Due to the exposure to different kinds of uncertainties and unpredictable faults, how to reliably assess the short-term voltage stability (SVS)
Yonghong Luo, Chao Lu, Lipeng Zhu, Jie Song   +3 more
semanticscholar   +1 more source

Waveform Measurement Unit-Based Fault Location in Distribution Feeders via Short-Time Matrix Pencil Method and Graph Neural Network

IEEE transactions on industry applications, 2023
This article proposes the use of the Short-Time Matrix Pencil method (STMPM) and Graph Neural Network (GNN) for fault location in active distribution feeders based on an emerging class of sensors, known as Waveform Measurement Units (WMUs).
Mohammad MansourLakouraj, Hadi Hosseinpour, H. Livani, Mohammed Benidris   +3 more
semanticscholar   +1 more source

Cooperative Fault-Estimation-Based Event-Triggered Fault-Tolerant Voltage Restoration in Islanded AC Microgrids

IEEE Transactions on Automation Science and Engineering, 2023
In this paper, the problem of secondary voltage restoration in an islanded microgrid (MG) is considered, in which the actuator of the distributed generators (DGs) may coexist with partial loss of effectiveness (PLOE) fault and bias fault. For each DG, an
Meina Zhai, Qiuye Sun, Bing Wang, Zhenwei Liu, Hua-guang Zhang   +4 more
semanticscholar   +1 more source

Real-Time Topology Estimation for Active Distribution System Using Graph-Bank Tracking Bayesian Networks

IEEE Transactions on Industrial Informatics, 2023
Real-time topology estimation in distribution grid with high penetration of distributed energy resources remains a challenging task due to the insufficient high-precision measurements and frequent topology variations.
You-bo Liu, Peng Ren, Junbo Zhao, Tingjian Liu, Zeqi Wang, Zao Tang, Junyong Liu   +6 more
semanticscholar   +1 more source

Non-Intrusive Load Monitoring Using Identity Library Based on Structured Feature Graph and Group Decision Classifier

IEEE Transactions on Smart Grid, 2023
Non-intrusive load monitoring (NILM) is performed to realize intelligent power consumption. A load identification algorithm which is flexible for various households is required to realize automatic NILM.
Xin Wu, Y. Guo, Meng Yan, Xiang Li, Lijuan Yao, Gangjun Gong   +5 more
semanticscholar   +1 more source

On Modeling Voltage Phasor Measurements as Graph Signals

Data Science Workshop, 2019
While the graph theoretic properties pertaining to the electrical grid are well known, the field of graph signal processing offers new insights and understanding about the measurements from the electrical grid.
Raksha Ramakrishna, A. Scaglione
semanticscholar   +1 more source

Topology identification method for residential areas in low-voltage distribution networks based on unsupervised learning and graph theory

Electric power systems research, 2023
Haifeng Li, Wenzhao Liang, Yuansheng Liang, Zhikeng Li, G. Wang   +4 more
semanticscholar   +1 more source

Physics-Informed Graphical Representation-Enabled Deep Reinforcement Learning for Robust Distribution System Voltage Control

IEEE Transactions on Smart Grid
The anomalous measurements and inaccurate distribution system physical models cause huge challenges for distribution system optimization. This paper proposes a robust voltage control method that can deal with them by systematically integrating a ...
Di Cao, Junbo Zhao, Jiaxiang Hu, Yansong Pei, Qi Huang, Zhe Chen, Weihao Hu   +6 more
semanticscholar   +1 more source

Multi-Agent Safe Graph Reinforcement Learning for PV Inverters-Based Real-Time Decentralized Volt/Var Control in Zoned Distribution Networks

IEEE Transactions on Smart Grid
To realize real-time voltage/var control (VVC) in active distribution networks (ADNs), this paper proposes a new multi-agent safe graph reinforcement learning method to optimize reactive power output from PV inverters. The network is divided into several
Rudai Yan, Q. Xing, Yan Xu
semanticscholar   +1 more source