Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-22
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil.
In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method.
The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy.
Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.
American Psychological Association (APA)
Ding, Yu& Liu, Qiang. 2017. Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-5.
https://search.emarefa.net/detail/BIM-1190060
Modern Language Association (MLA)
Ding, Yu& Liu, Qiang. Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model. Mathematical Problems in Engineering No. 2017 (2017), pp.1-5.
https://search.emarefa.net/detail/BIM-1190060
American Medical Association (AMA)
Ding, Yu& Liu, Qiang. Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-5.
https://search.emarefa.net/detail/BIM-1190060
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190060