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Hybrid Condition Monitoring System for Power Transformer Fault Diagnosis

Author

Listed:
  • Engin Baker

    (Department of Electrical and Electronics Engineering, Institute of Pure and Applied Sciences, Marmara University, Istanbul 34722, Turkey)

  • Secil Varbak Nese

    (Electrical and Electronics Engineering, Faculty of Technology, Marmara University, Istanbul 34854, Turkey)

  • Erkan Dursun

    (Electrical and Electronics Engineering, Faculty of Technology, Marmara University, Istanbul 34854, Turkey)

Abstract

The important parts of a transformer, such as the core, windings, and insulation materials, are in the oil-filled tank. It is difficult to detect faults in these materials in a closed area. Dissolved Gas Analysis (DGA)-based fault diagnosis methods predict a fault that may occur in the transformer and take the necessary precautions before the fault grows. Although these fault diagnosis methods have an accuracy of over 95%, their validity is controversial since limited data are used in the studies. The success rates and reliability of fault diagnosis methods in transformers, one of the most important pieces of power systems equipment, should be increased. In this study, a hybrid fault diagnosis system is designed using DGA-based methods and Fuzzy Logic. A mathematical approach and support vector machines (SVMs) were used as decision-making methods in the hybrid fault diagnosis systems. The results of tests performed with 317 real fault data sets relating to transformers showed accuracy of 95.58% using a mathematical approach and 96.23% using SVMs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1151-:d:1042474
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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