Intelligent Fault Diagnosis for Power Transformer Based on DGA Data Using Support Vector Machine (SVM)

Arian Dhini, Isti Surjandari, Akhmad Faqih, Benyamin Kusumoputro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Transformer is a crucial element in distributing electricity from power plant. Disturbance in transformer operation should be avoided. Dissolved gas analysis (DGA) has been known as one of the most effective tools to monitor the health of transformer. There are various methods in interpreting DGA manually, such as IEEE and IEC-based methods. However, those methods still require the human expertise. Fast and accurate fault diagnosis in the transformer remains a challenge. This study proposes an intelligent system to diagnose fault types in the transformer using data mining approach, i.e. support vector machine (SVM). SVM has been known for its robustness, good generalization ability and unique global optimum solutions. IEC TC10 databases are used as data to illustrate the performance of multistage support vector machine (SVM). The proposed system yields effective transformer fault diagnosis with high recognition rate, which is around 90%.

Original languageEnglish
Title of host publicationProceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages294-298
Number of pages5
ISBN (Electronic)9781728102382
DOIs
Publication statusPublished - 11 Apr 2019
Event3rd International Conference on System Reliability and Safety, ICSRS 2018 - Barcelona, Spain
Duration: 24 Nov 201826 Nov 2018

Publication series

NameProceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018

Conference

Conference3rd International Conference on System Reliability and Safety, ICSRS 2018
CountrySpain
CityBarcelona
Period24/11/1826/11/18

Keywords

  • condition monitoring
  • Dissolved gas analysis
  • fault diagnosis
  • support vector machine
  • transformer

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  • Cite this

    Dhini, A., Surjandari, I., Faqih, A., & Kusumoputro, B. (2019). Intelligent Fault Diagnosis for Power Transformer Based on DGA Data Using Support Vector Machine (SVM). In Proceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018 (pp. 294-298). [8688719] (Proceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSRS.2018.8688719