Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

Chun Yan, Meixuan Li and Wei Liu

Mathematical Problems in Engineering, 2019, vol. 2019, 1-10

Abstract:

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 (H 2 ), methane (CH 4 ), acetylene (C 2 H 2 ), ethane (C 2 H 6 ), and ethylene (C 2 H 4 ). 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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2019/1019845.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2019/1019845.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1019845

DOI: 10.1155/2019/1019845

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().