NDLI: Fault diagnosis model for power transformer based on statistical learning theory and dissolved gas analysis

Content Provider	IEEE Xplore Digital Library
Author	Dong, M. Xu, D.K. Li, M.H. Yan, Z.
Copyright Year	2004
Description	Author affiliation: Sch. of Electr. Eng., Xi'an Jiaotong Univ., China (Dong, M.)
Abstract	After thoroughly analyzing the relationships between indications and faults, it has been found that there are no explicit mapping functions between the faults of oil-immersed power transformer. To handle this problem, a multilevel decision-making model for power transformer fault diagnosis based on statistical learning theory is presented. Based on the concentration distribution of some typical fault gases, the proposed approach is to determine the optimal solution with a few training samples. The output of this model is improved by approaching exactly with K-nearest neighbor search classification for the SVM classification results, which is adjacent to optimal separating hyperplan. So the dependability of this model is enhanced greatly, and its effectiveness and usefulness is proved.
Starting Page	85
Ending Page	88
File Size	274049
Page Count	4
File Format	PDF
ISBN	0780384474
ISSN	1089084X
DOI	10.1109/ELINSL.2004.1380462
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Power transformers Statistical learning Dissolved gas analysis Support vector machines Gases Support vector machine classification Power system reliability Petroleum Power system modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Transformer fault diagnosis based on euclidean clustering and support vector machines

Transformer Fault Diagnosis Based on Improved SVM Model

Transformer Fault Diagnosis Based on Naive Bayesian Classifier and SVR

Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier

Design of Power Transformer Fault Diagnosis Model Based on Support Vector Machine

Fault diagnosis of power transformer using LS-SVMs with BCC

Fault diagnosis and system development of power transformer based on support vector machine

Multi-tie SVMs Classifier based Power Equipment Fault Diagnosis

The application of chaos support vector machines in transformer fault diagnosis

Fault diagnosis model for power transformer based on statistical learning theory and dissolved gas analysis

Similar Documents

Transformer fault diagnosis based on euclidean clustering and support vector machines

Transformer Fault Diagnosis Based on Improved SVM Model

Transformer Fault Diagnosis Based on Naive Bayesian Classifier and SVR

Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier

Design of Power Transformer Fault Diagnosis Model Based on Support Vector Machine

Fault diagnosis of power transformer using LS-SVMs with BCC

Fault diagnosis and system development of power transformer based on support vector machine

Multi-tie SVMs Classifier based Power Equipment Fault Diagnosis

The application of chaos support vector machines in transformer fault diagnosis

Fault diagnosis model for power transformer based on statistical learning theory and dissolved gas analysis