Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF-Reference-Cited by-同舟云学术

Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF

Author:

Zeng Lanting¹^ORCID,Sun Mingyang¹^ORCID,Wan Xu¹,Zhang Zhenyong²^ORCID,Deng Ruilong¹^ORCID,Xu Yan³^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. State Key Laboratory of Public Big Data and the College of Computer Science and Technology, Guizhou University, Guiyang, China

3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

Reference39 articles.

1. L2rpn challenge competitions,2019

5. Winning the L2RPN challenge: Power grid management via semi-markov afterstate actor-critic;yoon;Proc Int Conf Learn Representations,0

Cited by 16 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