Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization-Reference-Cited by-同舟云学术

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Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization

Published:2021-07 Issue:4 Volume:12 Page:3613-3623
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Zheng Yan^ORCID,Yan Ziming^ORCID,Chen Kangjie^ORCID,Sun Jianwen^ORCID,Xu Yan^ORCID,Liu Yang^ORCID

Funder

Singapore National Research Foundation, through the National Cybersecurity Research and Development Program

Singapore National Research Foundation

NRF Investigatorship

Ministry of Education, Republic of Singapore

Nanyang Assistant Professorship from Nanyang Technological University, Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/9460803/09365691.pdf?arnumber=9365691

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust Data-Driven Adversarial False Data Injection Attack Detection Method With Deep Q-Network in Power Systems;IEEE Transactions on Industrial Informatics;2024-08

2. Operation of distribution network: Challenges and opportunities in the era of peer-to-peer trading;Energy Reports;2024-06

3. Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review;IEEE Internet of Things Journal;2024-06-01

4. Heterogeneous reinforcement learning for defending power grids against attacks;APL Machine Learning;2024-06-01

5. A Review and Analysis of Attack and Countermeasure Approaches for Enhancing Smart Grid Cybersecurity;2024 28th International Electrical Power Distribution Conference (EPDC);2024-04-23

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