Enhancing Adaptability of Restoration Strategy for Distribution Network: A Meta-Based Graph Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Enhancing Adaptability of Restoration Strategy for Distribution Network: A Meta-Based Graph Reinforcement Learning Approach

Published:2024-07-15 Issue:14 Volume:11 Page:25440-25453
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Fan Bangji¹^ORCID,Liu Xinghua¹^ORCID,Xiao Gaoxi²^ORCID,Yang Xiang¹^ORCID,Chen Badong³^ORCID,Wang Peng²^ORCID

Affiliation:

1. School of Electrical Engineering, Xi’an University of Technology, Xi’an, China

2. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

3. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Shaanxi Outstanding Youth Science Fund Project

High Level Talents Plan of Shaanxi Province for Young Professionals

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10589449/10518131.pdf?arnumber=10518131

Reference48 articles.

1. A Comprehensive Resilience-Oriented FLISR Method for Distribution Systems

2. Evaluation for Risk of Cascading Failures in Power Grids by Inverse-Community Structure

3. Research on Resilience of Power Systems Under Natural Disasters—A Review

4. Battling the Extreme: A Study on the Power System Resilience

5. Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System With IoTs

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