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

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.

Fault diagnosis of transformer using artificial intelligence: A review

“…This method improves the accuracy of fault 10.3389/fenrg.2022.1006474 diagnosis and has strong robustness. In addition, researchers have also combined computational intelligence techniques such as bat algorithm (BA) (Benmahamed et al, 2021) and gray wolf algorithm (Zeng et al, 2019) with SVM to obtain transformer fault diagnosis models with good results.…”

“…Intelligent techniques help to resolve the uncertainty of traditional DGA methods due to boundary problems and unresolved codes or multi-fault scenarios (Wani et al, 2021). Researchers have applied many artificial intelligence techniques to DGA fault diagnosis, such as neural networks (Duan and Liu, 2011;Wang et al, 2016;Qi et al, 2019;Yan et al, 2019;Yang et al, 2019Yang et al, , 2020Luo et al, 2020;Velásquez and Lara, 2020;Mi et al, 2021;Taha et al, 2021;Zhou et al, 2021), support vector machine (SVM) (Wang and Zhang, 2017;Fang et al, 2018;Huang et al, 2018;Illias and Liang, 2018;Kari et al, 2018;Kim et al, 2019;Zeng et al, 2019;Zhang et al, 2019;Zhang Y. et al, 2020;Benmahamed et al, 2021), and clustering (Islam et al, 2017;Misbahulmunir et al, 2020). These techniques involve statistical machine learning, deep learning, etc.…”

Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases

“…Various DGA standard interpretation methods may lead to different results, which makes the final decision difficult. Several solutions are proposed in the literature based on the combination of more than one technique for improving the accuracy of the DGA techniques To overcome these limitations [26,27]. For example, Sayed A Ward et al [26] ).…”

Reliable Estimation for Health Index of Transformer Oil Based on Novel Combined Predictive Maintenance Techniques

“…Most of the existing DGA techniques have limited diagnostic accuracy and may fail to interpret the oil faults in transformers [13]. More studies have been presented to improve the traditional interpretation techniques using artificial intelligence (AI) and computational techniques [14][15][16]. However, most AI techniques are complicated and difficult for practical engineers to apply.…”

“…For power transformer, a new maintenance decision-making model is proposed based on economy and reliability assessment [22]. According to support vector machine (SVM), a novel technique is proposed to improve the diagnostic accuracy of transformer fault types [16]. DGA lacks two main issues.…”