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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2970-:d:558712
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    References listed on IDEAS

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    1. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
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    Cited by:

    1. Engin Baker & Secil Varbak Nese & Erkan Dursun, 2023. "Hybrid Condition Monitoring System for Power Transformer Fault Diagnosis," Energies, MDPI, vol. 16(3), pages 1-11, January.
    2. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    3. Zhi-Jun Li & Wei-Gen Chen & Jie Shan & Zhi-Yong Yang & Ling-Yan Cao, 2022. "Enhanced Distributed Parallel Firefly Algorithm Based on the Taguchi Method for Transformer Fault Diagnosis," Energies, MDPI, vol. 15(9), pages 1-22, April.
    4. Xiaoqin Zhang & Hongbin Zhu & Bo Li & Ruihan Wu & Jun Jiang, 2022. "Power Transformer Diagnosis Based on Dissolved Gases Analysis and Copula Function," Energies, MDPI, vol. 15(12), pages 1-14, June.
    5. Jannis N. Kahlen & Michael Andres & Albert Moser, 2021. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault," Energies, MDPI, vol. 14(20), pages 1-20, October.
    6. Abdessamed Derdour & Hazem Ghassan Abdo & Hussein Almohamad & Abdullah Alodah & Ahmed Abdullah Al Dughairi & Sherif S. M. Ghoneim & Enas Ali, 2023. "Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    7. Bonginkosi A. Thango, 2022. "Dissolved Gas Analysis and Application of Artificial Intelligence Technique for Fault Diagnosis in Power Transformers: A South African Case Study," Energies, MDPI, vol. 15(23), pages 1-17, November.
    8. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.

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