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Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-21
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
A new method of transformer fault diagnosis based on relevance vector machine (RVM) is proposed.
Bayesian estimation is applied to support vector machine (SVM) in the novel algorithm, which made fault diagnosis system work more effectively.
In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples.
The process of transformer fault diagnosis for four working statuses is given in experiments and simulations.
The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP) neural networks and general SVM methods.
American Psychological Association (APA)
Liu, Lutao& Ding, Zujun. 2013. Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1010171
Modern Language Association (MLA)
Liu, Lutao& Ding, Zujun. Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine. Mathematical Problems in Engineering No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-1010171
American Medical Association (AMA)
Liu, Lutao& Ding, Zujun. Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1010171
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010171