A Novel Integral Reinforcement Learning-Based Control Method Assisted by Twin Delayed Deep Deterministic Policy Gradient for Solid Oxide Fuel Cell in DC Microgrid-Reference-Cited by-同舟云学术

A Novel Integral Reinforcement Learning-Based Control Method Assisted by Twin Delayed Deep Deterministic Policy Gradient for Solid Oxide Fuel Cell in DC Microgrid

Published:2023-01 Issue:1 Volume:14 Page:688-703
ISSN:1949-3029
Container-title:IEEE Transactions on Sustainable Energy
language:
Short-container-title:IEEE Trans. Sustain. Energy

Author:

Liu Yulin¹^ORCID,Qie Tianhao¹^ORCID,Yu Yang²^ORCID,Wang Yuxuan¹,Chau Tat Kei¹^ORCID,Zhang Xinan¹^ORCID,Manandhar Ujjal³^ORCID,Li Sinan⁴^ORCID,H. C. Iu Herbert¹^ORCID,Fernando Tyrone¹^ORCID

Affiliation:

1. School of Engineering, University of Western Australia, Crawley, WA, Australia

2. Center of Excellence in Advanced Control, Halliburton Ltd, Singapore

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

4. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

Funder

University of Western Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Renewable Energy, Sustainability and the Environment

Link

http://xplorestaging.ieee.org/ielx7/5165391/9994274/09961949.pdf?arnumber=9961949

Reference27 articles.

1. Voltage control of solid oxide fuel cell power plant based on intelligent proportional integral-adaptive sliding mode control with anti-windup compensator;abbaker;Trans Inst Meas Control,2019

2. Designing Hydrogen and Oxygen Flow Rate Control on a Solid Oxide Fuel Cell Simulator Using the Fuzzy Logic Control Method

3. A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning

4. A novel data-driven controller for solid oxide fuel cell via deep reinforcement learning

5. Optimal control strategy for solid oxide fuel cell‐based hybrid energy system using deep reinforcement learning

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