Graph Attention Enabled Convolutional Network for Distribution System Probabilistic Power Flow-Reference-Cited by-同舟云学术

Graph Attention Enabled Convolutional Network for Distribution System Probabilistic Power Flow

Published:2022-11 Issue:6 Volume:58 Page:7068-7078
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Wu Huayi¹^ORCID,Wang Minghao²^ORCID,Xu Zhao¹^ORCID,Jia Youwei³^ORCID

Affiliation:

1. PolyU Research Institute for Smart Energy (RISE) and the Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

2. PolyU Research Institute for Smart Energy (RISE) and Power Electronics Research Center, The Hong Kong Polytechnic University, Hong Kong SAR, China

3. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

Hong Kong Research Grants Council

General Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/28/9956958/09868149.pdf?arnumber=9868149

Reference38 articles.

1. Solving stochastic AC power flow via polynomial chaos expansion

2. Hydrogen Energy Storage System for Demand Forecast Error Mitigation and Voltage Stabilization in a Fast-Charging Station

3. Reactive power compensation in distribution networks using cuckoo search algorithm

4. Distribution Network Reconfiguration for Loss Reduction and Voltage Stability With Random Fuzzy Uncertainties of Renewable Energy Generation and Load

5. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

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