IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2700-d758619.html
   My bibliography  Save this article

An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants

Author

Listed:
  • Wenmin Yu

    (School of Nuclear Science and Techniques, Naval University of Engineering, Wuhan 430034, China)

  • Ren Yu

    (School of Nuclear Science and Techniques, Naval University of Engineering, Wuhan 430034, China)

  • Jun Tao

    (China Nuclear Power Operation Management Co., Ltd., Jiaxing 314300, China)

Abstract

Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas in oil are widely installed in main transformers, but there are few recorded fault data of main transformers. The special operation and maintenance modes of main transformers leads to the fault modes particularity of main transformers. In order to solve the problem of insufficient samples and the feature uncertainty, this paper puts forward an unsupervised mutual information method to select the feature verified by the optimized support vector machine (SVM) model of particle swarm optimization (PSO) method and tries to find the feature sequence with better performance. The methos is validated by data from nuclear power transformers.

Suggested Citation

  • Wenmin Yu & Ren Yu & Jun Tao, 2022. "An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants," Sustainability, MDPI, vol. 14(5), pages 1-10, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2700-:d:758619
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2700/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/5/2700/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hazlee Azil Illias & Wee Zhao Liang, 2018. "Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
    Full references (including those not matched with items on IDEAS)

    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. Amran Mohd Selva & Norhafiz Azis & Muhammad Sharil Yahaya & Mohd Zainal Abidin Ab Kadir & Jasronita Jasni & Young Zaidey Yang Ghazali & Mohd Aizam Talib, 2018. "Application of Markov Model to Estimate Individual Condition Parameters for Transformers," Energies, MDPI, vol. 11(8), pages 1-16, August.
    2. Alexandra I. Khalyasmaa & Pavel V. Matrenin & Stanislav A. Eroshenko & Vadim Z. Manusov & Andrey M. Bramm & Alexey M. Romanov, 2022. "Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    3. 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.

    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:jsusta:v:14:y:2022:i:5:p:2700-:d:758619. 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.