Gallium Nitride Power Electronic Devices Modeling Using Machine Learning

Gallium Nitride Power Electronic Devices Modeling Using Machine Learning

A state-of-the-art Machine Learning (ML) based approach, by modeling the behavior of Gallium Nitride (GaN) power electronic devices, is presented in this paper. Switching voltage and current waveforms of these novel devices are accurately predicted using the developed supervised ML algorithm. This w...

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Bibliographic Details
Main Authors: Nikita Hari, Mominul Ahsan, Sridhar Ramasamy, Padmanaban Sanjeevikumar, Alhussein Albarbar, Frede Blaabjerg
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Gallium Nitride
power electronics
modelling
machine learning
neural networks
Online Access:https://ieeexplore.ieee.org/document/9127426/

Internet

https://ieeexplore.ieee.org/document/9127426/

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