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

Youcef Benmahamed, Omar Kherif, Madjid Teguar, Ahmed Boubakeur and Sherif S. M. Ghoneim
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Youcef Benmahamed: Research Laboratories, National Polytechnic School (ENP), B.P 182, El-Harrach, Algiers 16200, Algeria
Omar Kherif: Research Laboratories, National Polytechnic School (ENP), B.P 182, El-Harrach, Algiers 16200, Algeria
Madjid Teguar: Research Laboratories, National Polytechnic School (ENP), B.P 182, El-Harrach, Algiers 16200, Algeria
Ahmed Boubakeur: Research Laboratories, National Polytechnic School (ENP), B.P 182, El-Harrach, Algiers 16200, Algeria
Sherif S. M. Ghoneim: Electrical Engineering Department, College of Engineering, Taif University, Taif 21944, Saudi Arabia

Energies, 2021, vol. 14, issue 10, 1-17

Abstract: 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.

Keywords: transformer faults; SVM-BA classifier; DGA; DGALab (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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