Deep Reinforcement Learning-Based Optimal Control of DC Shipboard Power Systems for Pulsed Power Load Accommodation-Reference-Cited by-同舟云学术

Deep Reinforcement Learning-Based Optimal Control of DC Shipboard Power Systems for Pulsed Power Load Accommodation

Published:2023-01 Issue:1 Volume:14 Page:29-40
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Tu Zhenghong¹^ORCID,Zhang Wei¹^ORCID,Liu Wenxin¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Smart Microgrid and Renewable Technology Research Laboratory, Lehigh University, Bethlehem, PA, USA

Funder

U.S. Office of Naval Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/9997165/09847360.pdf?arnumber=9847360

Reference31 articles.

1. A Medium Voltage DC Testbed for ship power system research

2. Performance Guaranteed Control of Flywheel Energy Storage System for Pulsed Power Load Accommodation

3. Reducing Impact of Pulsed Power Loads on Microgrid Power Systems

4. System impact of pulsed power loads on a laboratory scale integrated fight through power (IFTP) system

5. Policy iterations for reinforcement learning problems in continuous time and space — Fundamental theory and methods

Cited by 11 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. Distributed Remote Secure ADP-Based Control for Interconnected Power Systems Under Cyber-Attacks;IEEE Internet of Things Journal;2024-06-15

2. NAIR: An Efficient Distributed Deep Learning Architecture for Resource Constrained IoT System;IEEE Internet of Things Journal;2024-06-15

3. AI-Enabled Cooperative Control for DC Microgrids of Battery Storage System;2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia);2024-05-17

4. Trajectory simulation and optimization for interactive electricity-carbon system evolution;Applied Energy;2024-04

5. Data-Efficient Deep Reinforcement Learning-Based Optimal Generation Control in DC Microgrids;IEEE Systems Journal;2024-03