A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

This paper presents a Dynamic Adam and dropout based deep neural network (DADDNN) for fault diagnosis of oil-immersed power transformers. To solve the problem of incomplete extraction of hidden information with data driven, the gradient first-order moment estimate and second-order moment estimate ar...

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Bibliographic Details
Main Authors: Minghui Ou, Hua Wei, Yiyi Zhang, Jiancheng Tan
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Energies
Subjects:
power transformer
fault diagnosis
dissolved gas analysis
deep neural network
Dynamic Adam
dropout
Online Access:http://www.mdpi.com/1996-1073/12/6/995

Internet

http://www.mdpi.com/1996-1073/12/6/995

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