Application of CP Modular Neural Networks on DGA Based Power Transformer Fault Diagnosis

Author

Xiao-ming, Wang ; Xiaobo, Liu ; Yongping, Lu

Author_Institution

Ultra High Voltage Troansmission Subcompany, Jiangxi Electr. Power Corp., Nangchang, China

fYear

2008

fDate

9-12 Nov. 2008

Firstpage

574

Lastpage

576

Abstract

Counter propagation arithmetic is make up of this paper presents a fault diagnosis model for power transformer based on counter propagation network. The compound neural networks model is build first and parameters of CP Networks are confirmed by comparing the results in different situations. The diagnostic examples indicate the validity of the proposed method. The diagnosis correctness of the new method has been much enhanced than the ordinary BP neural network and the improved three rations method.

Keywords

backpropagation; fault diagnosis; neural nets; power transformer testing; CP modular neural networks; backpropagation; counter propagation arithmetic; diagnosis correctness; dissolved gases analysis; power transformer fault diagnosis; Artificial neural networks; Counting circuits; Dissolved gas analysis; Fault diagnosis; Neural networks; Neurons; Oil insulation; Power system security; Power transformer insulation; Power transformers; CP compound neural networks; Dissolved Gases Analysis (DGA); Fault diagnosis; Transformer;

fLanguage

English

Publisher

ieee

Conference_Titel

High Voltage Engineering and Application, 2008. ICHVE 2008. International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-3823-5

Electronic_ISBN

978-1-4244-2810-6

Type

conf

DOI

10.1109/ICHVE.2008.4774000

Filename

4774000