The Application of Compound Networks in Fault Diagnosis of Power Transformer
Using the concepts of typical gass concentration and cumulative frequency in analysis of the reliability data for dealing with the pretreatment of data of DGA, two new normalized methods which named characteristic normalization and mix normalization are presented in this paper. The Fisher rule to evaluate the results of the two pretreatment methods is also introduced. The evaluation of the results indicates that both of the two data pretreatment methods can achieve the purpose of big difference in the value of mean between classes and small difference in dispersion of a class. The DGA data of the failure transformers are treated by different normalization methods as the training samples, and then the samples are trained in the compound neural networks which use the CP algorithm. The diagnosis results of the test samples indicate that the new methods may help to improve the precision of network diagnosis.
transformer analysis of reliability data CP compound neural networks fault diagnosis
ZHANG Wei-zheng WANG Zheng-gang RONG Jun KUANG Shi ZHANG GuiXin
Zhengzhou Power Supply Company,Zhengzhou 450000,China Department of Electrical Engineering and Applied Electronic Technology,Tsinghua University,Beijing 1
国际会议
2008 China International Conference on Electricity Distribution (CICED 2008)(2008中国国际供电会议)
广州
英文
1-5
2008-12-10（万方平台首次上网日期，不代表论文的发表时间）