Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Mounia Hendel, Fethi Meghnefi, Mohamed El Amine Senoussaoui, Issouf Fofana () and Mostefa Brahami
Additional contact information
Mounia Hendel: LGEM, Ecole Supérieure en Génie Electrique et Energétique d’Oran, Oran 31000, Algeria
Fethi Meghnefi: Canada Research Chair, Tier 1, ViAHT, University Québec, Chicoutimi, QC G7H 2B1, Canada
Mohamed El Amine Senoussaoui: LGPCS, University Mustapha Stambouli of Mascara, Mascara 29000, Algeria
Issouf Fofana: Canada Research Chair, Tier 1, ViAHT, University Québec, Chicoutimi, QC G7H 2B1, Canada
Mostefa Brahami: ICEPS, Faculty of Electrical Engineering, Djilali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes 22000, Algeria

Sustainability, 2023, vol. 15, issue 21, 1-23

Abstract: Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% ( p -value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

Keywords: DGA; probabilistic M-SVM; Generic M-SVM Model; Dempster–Shafer Rule; Kohonen Map (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/21/15453/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/21/15453/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:21:p:15453-:d:1270901

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().