skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier

Energies (Basel), 2021-05, Vol.14 (10), p.2970 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14102970

Full text available

Citations Cited by
  • Title:
    Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier
  • Author: Benmahamed, Youcef ; Kherif, Omar ; Teguar, Madjid ; Boubakeur, Ahmed ; Ghoneim, Sherif S. M.
  • Subjects: Accuracy ; Acetylene ; Algorithms ; Artificial intelligence ; Classifiers ; Decomposition ; DGA ; DGALab ; Diagnostic systems ; Dissolved gases ; Ethane ; Flammability ; Gas analysis ; Gases ; Hydrocarbons ; Neural networks ; Parameters ; Probability ; Support vector machines ; SVM-BA classifier ; transformer faults
  • Is Part Of: Energies (Basel), 2021-05, Vol.14 (10), p.2970
  • Description: The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter “λ” and penalty margin parameter “C” were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers’ accuracy was evaluated and compared with several DGA techniques in the literature.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1996-1073
    EISSN: 1996-1073
    DOI: 10.3390/en14102970
  • Source: DOAJ Directory of Open Access Journals
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    ROAD: Directory of Open Access Scholarly Resources
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait