Deep Neural Network-Based Surrogate Model for Optimal Component Sizing of Power Converters Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Deep Neural Network-Based Surrogate Model for Optimal Component Sizing of Power Converters Using Deep Reinforcement Learning

Published:2022 Issue: Volume:10 Page:78702-78712
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Bui Van-Hai¹,Chang Fangyuan¹^ORCID,Su Wencong¹^ORCID,Wang Mengqi¹^ORCID,Murphey Yi Lu¹^ORCID,Da Silva Felipe Leno²,Huang Can²,Xue Lingxiao³^ORCID,Glatt Ruben²

Affiliation:

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

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

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

Funder

Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy

U.S. Department of Energy, Lawrence Livermore National Laboratory

Lawrence Livermore National Security through LLC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09841555.pdf?arnumber=9841555

Reference27 articles.

1. The Implementation of Bee Colony Optimization Algorithm to Sheppard–Taylor PFC Converter

2. AI Algorithm-Based Two-Stage Optimal Design Methodology of High-Efficiency CLLC Resonant Converters for the Hybrid AC–DC Microgrid Applications

3. Multiobjective Optimization of Medium-Frequency Transformers for Isolated Soft-Switching Converters Using a Genetic Algorithm

4. From Specification to Topology: Automatic Power Converter Design via Reinforcement Learning

5. Model-Free Reinforcement-Learning-Based Control Methodology for Power Electronic Converters

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