IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2970-d558712.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2970/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/10/2970/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. George Kimani Irungu & Aloys Oriedi Akumu, 2020. "Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment," Energies, MDPI, vol. 13(18), pages 1-24, September.
    2. Bustamante, Sergio & Manana, Mario & Arroyo, Alberto & Laso, Alberto & Martinez, Raquel, 2024. "Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    3. Firas B. Ismail & Maisarah Mazwan & Hussein Al-Faiz & Marayati Marsadek & Hasril Hasini & Ammar Al-Bazi & Young Zaidey Yang Ghazali, 2022. "An Offline and Online Approach to the OLTC Condition Monitoring: A Review," Energies, MDPI, vol. 15(17), pages 1-18, September.
    4. Michel Duval & Constantin Ene, 2021. "Identification of Stray Gassing of Dodecylbenzene in Bushings," Energies, MDPI, vol. 14(9), pages 1-7, April.
    5. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak & Zbigniew Szymanski, 2020. "Using the Analysis of the Gases Dissolved in Oil in Diagnosis of Transformer Bushings with Paper-Oil Insulation—A Case Study," Energies, MDPI, vol. 13(24), pages 1-12, December.
    6. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    7. 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.
    8. James Dukarm & Zachary Draper & Tomasz Piotrowski, 2020. "Diagnostic Simplexes for Dissolved-Gas Analysis," Energies, MDPI, vol. 13(23), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2970-:d:558712. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.