NDLI: SVM-based decision for power transformers fault diagnosis using Rogers and Doernenburg ratios DGA

Content Provider	IEEE Xplore Digital Library
Author	Souahlia, S. Bacha, K. Chaari, A.
Copyright Year	2013
Description	Author affiliation: Control, Monitoring & Reliability of the Syst., Higher Sch. of Sci. & Technol. of Tunis, Tunis, Tunisia (Souahlia, S.; Bacha, K.; Chaari, A.)
Abstract	Dissolved gas analysis (DGA) is a widely-used method to detect the power transformer faults, because of its high sensitivity to small amount of electrical faults. The DGA is exploited for fault classification tools implementation using the artificial intelligence techniques. In this study, we use the Rogers ratios and the Doernenburg ratios DGA methods as gas signature. The Support vector machine (SVM) is powerful for the problem with small sampling (small amounts of training data), nonlinear and high dimension (large amounts of input data). The paper presents a comparative study on one hand for the choice the most appropriate DGA method between the Rogers and Doernenburg ratios methods. On the other hand, it compares the various SVM architectures by comparing the kernel functions types with the aim to establish the most appropriate SVM model. Before testing, the proposed structures are trained and tested by the experimental data from Tunisian Company of Electricity and Gas (STEG). The test results suggest that SVM Rogers model can generalize better than SVM Doernenburg model. The approach has the advantages of high accuracy. The other advantage is that the model is practically applicable and may be utilized for an automated power transformer diagnosis. The classification accuracies of the SVM classifier are compared with fuzzy logic (FL), radial basis function (RBF), K-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.
Sponsorship	IEEE Instrum. Meas. Soc.
Starting Page	1
Ending Page	6
File Size	1329862
Page Count	6
File Format	PDF
ISBN	9781467364591
e-ISBN	9781467364577
e-ISBN	9781467364584
DOI	10.1109/SSD.2013.6564073
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-18
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Terminology Transformer fault diagnosis Support vector machine classification Rogers ratios Doernenburg ratios Dissolved gas analysis Polynomials Vectors Thermal analysis Support vector machine Thermal stability
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 Fault Diagnosis Based on Improved SVM Model

A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis

TRANSFORMER FAULT CLASSIFICATION USING SUPPORT VECTOR MACHINE METHOD

Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier

Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection

Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)

Transformer fault diagnosis based on euclidean clustering and support vector machines

Fault diagnosis of power transformer using LS-SVMs with BCC

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

SVM-based decision for power transformers fault diagnosis using Rogers and Doernenburg ratios DGA

Similar Documents

Transformer Fault Diagnosis Based on Improved SVM Model

A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis

TRANSFORMER FAULT CLASSIFICATION USING SUPPORT VECTOR MACHINE METHOD

Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier

Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection

Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)

Transformer fault diagnosis based on euclidean clustering and support vector machines

Fault diagnosis of power transformer using LS-SVMs with BCC

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

SVM-based decision for power transformers fault diagnosis using Rogers and Doernenburg ratios DGA