NDLI: Health assessment model of power transformer based on dissolved gas analysis by support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Lian Chao Ma Lin
Copyright Year	2013
Description	Author affiliation: Sci. & Technol. on Reliability & Environ. Eng. Lab., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China (Lian Chao; Ma Lin)
Abstract	Power transformer in grid is of vital importance to guarantee safe operation of the power system. Nowadays dissolved gas analysis (DGA) is recognized as an effective method for detecting the initial fault of power transformer. Support vector machine (SVM) with Structure Risk Minimization (SRM) is powerful for the problem with small sampling, nonlinear and high dimension, which is based on mathematics theory. This paper establishes health assessment model based on DGA by using SVM. Health estimation is used to deal with the initial fault timely and prevent fault diffusion and dissemination. Then we choose appropriate kernel function and corresponding parameter to ensure the availability. Finally, a case is studied. The result indicates that the model can effectively estimate the health state of power transformer.
Starting Page	280
Ending Page	283
File Size	142026
Page Count	4
File Format	PDF
ISBN	9781479939855
e-ISBN	9781479902453
DOI	10.1109/ICIII.2013.6702929
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training power transformer health assessment support vector machine Oil insulation Mathematical model Kernel Power transformer insulation dissolved gas analysis
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	Dr. B. Sutradhar bsutra@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	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library 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 diagnostics under dissolved gas analysis using Support Vector Machine

Transformer Fault Diagnosis Based on Improved SVM Model

Transformer Dissolved Gas Analysis Using Least Square Support Vector Machine and Bootstrap

Transformer Fault Diagnosis Utilizing Rough Set and Support Vector Machine

Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier

Power Transformer Fault Diagnosis by Using the Artificial Immune Support Vector Machines

Machine learning techniques for power transformer insulation diagnosis

Fault diagnosis model of power transformer based on combinatorial KFDA

Transformer fault prediction based on support vector machine

Health assessment model of power transformer based on dissolved gas analysis by support vector machine

Similar Documents

Transformer diagnostics under dissolved gas analysis using Support Vector Machine

Transformer Fault Diagnosis Based on Improved SVM Model

Transformer Dissolved Gas Analysis Using Least Square Support Vector Machine and Bootstrap

Transformer Fault Diagnosis Utilizing Rough Set and Support Vector Machine

Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier

Power Transformer Fault Diagnosis by Using the Artificial Immune Support Vector Machines

Machine learning techniques for power transformer insulation diagnosis

Fault diagnosis model of power transformer based on combinatorial KFDA

Transformer fault prediction based on support vector machine

Health assessment model of power transformer based on dissolved gas analysis by support vector machine