Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters-Reference-Cited by-同舟云学术

Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters

Published:2023-02-20 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Computing, Networking and Communications (ICNC)
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Author:

Bui Van-Hai¹,Chang Fangyuan¹,Su Wencong¹,Wang Mengqi¹,Murphey Yi Lu¹,Silva Felipe Leno Da²,Huang Can²,Xue Lingxiao³,Glatt Ruben²

Affiliation:

1. University of Michigan-Dearborn,Department of Electrical and Computer Engineering, College of Engineering and Computer Science,Dearborn,MI,USA,48128

2. Lawrence Livermore National Laboratory (LLNL),Livermore,CA,USA>,94550

3. Oak Ridge National Laboratory (ORNL),Oak Ridge,TN,USA,37830

Funder

Advanced Research Projects Agency

U.S. Department of Energy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10073968/10073976/10074355.pdf?arnumber=10074355

Reference11 articles.

1. Voltage regulation of DC-DC buck converters feeding CPLs via deep reinforcement learning;cui;IEEE Trans Circuits and Systems II Express Briefs,2021

2. Multiobjective optimization of medium-frequency transformers for isolated soft-switching converters using a genetic algorithm;garcia-bediaga;IEEE Trans Power Electronics,2016

3. IoT-Based DC/DC Deep Learning Power Converter Control: Real-Time Implementation

4. Boost Converter Controller Design Using Queen-Bee-Assisted GA

5. Multi-Objective Design of Output LC Filter for Buck Converter via the Coevolving-AMOSA Algorithm

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