IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8548-d973602.html
   My bibliography  Save this article

Feedforward Artificial Neural Network (FFANN) Application in Solid Insulation Evaluation Methods for the Prediction of Loss of Life in Oil-Submerged Transformers

Author

Listed:
  • Bonginkosi A. Thango

    (Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa)

Abstract

In this work, the application of a feed-forward artificial neural network (FFANN) in predicting the degree of polymerization (DP) and loss of life (LOL) in oil-submerged transformers by using the solid insulation evaluation method is presented. The solid insulation evaluation method is a reliable technique to assess and predict the DP and LOL as it furnishes bountiful information in examining the transformer condition. Herein, two FFANN models are proposed. The first model is based on predicting the DP when only the 2-Furaldehyde (2FAL) concentration measured from oil samples is available for new and existing transformers. The second FFANN model proposed is based on predicting the transformer LOL when the 2FAL and DP are available to the utility owner, typically for the transformer operating at a site where un-tanking the unit is a daunting and unfeasible task. The development encompasses constructing numerous FFANN designs and picking networks with superlative performance. The training and testing procedures databank is based on the dataset of the 2FAL and DP from a fleet of transformers and measured from laboratory analysis. The correlation coefficient of 0.964 was ascertained when the DP was predicted using the 2FAL measured in oil. In the FFANN model, a correlation coefficient of 0.999 against the practical data where one can make a reliable prediction of transformer LOL concerning 2FAL was generated and the amount of DP present produced. This model can be used to predict the DP and LOL of new and existing transformers at the manufacturer’s premises and operating in the field, respectively. To the knowledge of the authors, no research work has been published addressing the methods proposed in this work.

Suggested Citation

  • Bonginkosi A. Thango, 2022. "Feedforward Artificial Neural Network (FFANN) Application in Solid Insulation Evaluation Methods for the Prediction of Loss of Life in Oil-Submerged Transformers," Energies, MDPI, vol. 15(22), pages 1-11, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8548-:d:973602
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8548/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/22/8548/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vahid Behjat & Reza Emadifar & Mehrdad Pourhossein & U. Mohan Rao & Issouf Fofana & Reza Najjar, 2021. "Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators," Energies, MDPI, vol. 14(13), pages 1-13, July.
    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. Andrew Adewunmi Adekunle & Samson Okikiola Oparanti & Issouf Fofana, 2023. "Performance Assessment of Cellulose Paper Impregnated in Nanofluid for Power Transformer Insulation Application: A Review," Energies, MDPI, vol. 16(4), pages 1-32, February.
    2. Enze Zhang & Jiang Liu & Chaohai Zhang & Peijun Zheng & Yosuke Nakanishi & Thomas Wu, 2023. "State-of-Art Review on Chemical Indicators for Monitoring the Aging Status of Oil-Immersed Transformer Paper Insulation," Energies, MDPI, vol. 16(3), pages 1-31, January.
    3. Alexander S. Karandaev & Igor M. Yachikov & Andrey A. Radionov & Ivan V. Liubimov & Nikolay N. Druzhinin & Ekaterina A. Khramshina, 2022. "Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition," Energies, MDPI, vol. 15(10), pages 1-21, May.

    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:15:y:2022:i:22:p:8548-:d:973602. 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.