Deep Reinforcement Learning-Based Tie-Line Power Adjustment Method for Power System Operation State Calculation-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning-Based Tie-Line Power Adjustment Method for Power System Operation State Calculation

Published:2019 Issue: Volume:7 Page:156160-156174
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Xu Huating^ORCID,Yu Zhihong,Zheng Qingping,Hou Jinxiu,Wei Yawei,Zhang Zhijian

Funder

Adaptive Generation and Interactive Adjustment Technology of Power Grid Operation State Based on Online Data

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8600701/08882242.pdf?arnumber=8882242

Cited by 13 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. Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach;Frontiers in Energy Research;2024-05-30

2. Leveraging AI for Enhanced Power Systems Control: An Introductory Study of Model-Free DRL Approaches;IEEE Access;2024

3. Power System Operation Mode Calculation Based on Improved Deep Reinforcement Learning;Mathematics;2023-12-30

4. Improvement of Fuzzy Newton Power Flow Convergence;Energies;2023-12-13

5. Multi-Objective Power Adjustment for Coupling Transmission Section Based on Deep Reinforcement Learning;2023 4th International Conference on Advanced Electrical and Energy Systems (AEES);2023-12-01

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