Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults.
It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power.
In different transformer faults, the concentration of dissolved gases in oil is also inconsistent.
Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4).
This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault.
The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.
American Psychological Association (APA)
Yan, Chun& Li, Meixuan& Liu, Wei. 2019. Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1194196
Modern Language Association (MLA)
Yan, Chun…[et al.]. Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection. Mathematical Problems in Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1194196
American Medical Association (AMA)
Yan, Chun& Li, Meixuan& Liu, Wei. Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1194196
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194196