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...
Main Authors: | Minghui Ou, Hua Wei, Yiyi Zhang, Jiancheng Tan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-03-01
|
Series: | Energies |
Subjects: | |
Online Access: | http://www.mdpi.com/1996-1073/12/6/995 |
Similar Items
-
Fault Diagnosis of Oil-Immersed Transformers Based on the Improved Neighborhood Rough Set and Deep Belief Network
by: Xiaoyang Miao, et al.
Published: (2023-12-01) -
Transformer fault classification for diagnosis based on DGA and deep belief network
by: Dexu Zou, et al.
Published: (2023-11-01) -
Fault diagnosis of transformer using artificial intelligence: A review
by: Yan Zhang, et al.
Published: (2022-09-01) -
Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network
by: Wanjie Lu, et al.
Published: (2023-01-01) -
Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)
by: Arian Dhini, et al.
Published: (2020-04-01)