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

Youcef Benmahamed,Madjid Teguar,Sherif S M Ghoneim,Omar Kherif,Ahmed Boubakeur

doi:10.3390/en14102970

Youcef Benmahamed, Madjid Teguar + Show 3 more

Open Access

https://doi.org/10.3390/en14102970

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Journal: Energies	Publication Date: May 20, 2021
Citations: 42	License type: CC BY 4.0

Affiliation: Polytechnic School of Algiers, Taif University

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.

Highlights

The insulation system state of the power transformers is responsible for determining the transformers’ lifetime
The concentrations of these gases were used as an input vector to interpret the dissolved gas analysis (DGA) results in transformer oil, associated with six basic electrical and thermal faults [4,8]
Improved coupled techniques have been developed to diagnose multiple transformer faults and quantitatively indicate each fault’s likelihood (e.g., [14]). Artificial intelligence techniques such as artificial neural networks (ANN) can combine with the traditional DGA techniques to enhance the diagnostic accuracy of the transformer faults, such as the California State University Sacramento artificial neural network method (CSUS-ANN) [13]

Summary

Introduction

The insulation system state of the power transformers is responsible for determining the transformers’ lifetime. It is generally exposed to a couple of defects arising from overheating, paper carbonization, arcing, and discharges of low or high energy [1,2,3] These faults might accelerate the insulation degradation, affecting the transformer reliability and lifetime [4]. Improved coupled techniques have been developed to diagnose multiple transformer faults and quantitatively indicate each fault’s likelihood (e.g., [14]) Artificial intelligence techniques such as artificial neural networks (ANN) can combine with the traditional DGA techniques to enhance the diagnostic accuracy of the transformer faults, such as the California State University Sacramento artificial neural network method (CSUS-ANN) [13]. The current work presents a classification technique (SVM-BAT and Gaussian classifiers) to enhance the transformer faults’ diagnostic accuracy, which considers one of the new trends in condition monitoring and diagnostics of power system assets

Problem Formulation

SVM Classifier Coupled with BA

Experimental Work

Findings

Validation and Overall Accuracy of the Proposed SVM-BA Classifier