Lifetime Extension Approach Based on the Levenberg–Marquardt Neural Network and Power Routing of DC

Lifetime Extension Approach Based on the Levenberg–Marquardt Neural Network and Power Routing of DC–DC Converters

Published:2023-08 Issue:8 Volume:38 Page:10280-10291
ISSN:0885-8993
Container-title:IEEE Transactions on Power Electronics
language:
Short-container-title:IEEE Trans. Power Electron.

Author:

Zhang Jiusi¹^ORCID,Tian Jilun¹^ORCID,Alcaide Abraham M.²^ORCID,Leon Jose I.³^ORCID,Vazquez Sergio³^ORCID,Franquelo Leopoldo G.³^ORCID,Luo Hao¹^ORCID,Yin Shen⁴^ORCID

Affiliation:

1. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China

2. Department of Electronic Engineering, School of Engineers, University of Seville, Seville, Spain

3. Laboratory of Engineering for Energy and Environmental Sustainability, Universidad de Sevilla, Sevilla, Spain

4. Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, Norway

Funder

PID2020

MCIN/AEI

TED2021

European Union NextGenerationEU/PRTR

Contratación de Personal Investigador Doctor

European Social Fund and Junta de Andalucía

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/63/10159462/10124028.pdf?arnumber=10124028

Reference33 articles.

1. Power Routing: A New Paradigm for Maintenance Scheduling

2. Prediction Model With Harmonic Load Current Components for FCS-MPC of an Uninterruptible Power Supply

3. Variable-Angle PS-PWM Technique for Multilevel Cascaded H-Bridge Converters With Large Number of Power Cells

4. Electric Vehicle Charging Infrastructure: From Grid to Battery

5. A Parallel Hybrid Neural Network With Integration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics

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