IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6455-d1234374.html
   My bibliography  Save this article

Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging

Author

Listed:
  • Ahmed Sayadi

    (Laboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat 03000, Algeria
    Laboratory of Applied and Didactic Sciences, Higher Normal School of Laghouat, BP 4033, Laghouat 03000, Algeria)

  • Djillali Mahi

    (Laboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat 03000, Algeria)

  • Issouf Fofana

    (Research Chair on the Aging of Power Network Infrastructure (ViAHT), University of Québec, Saguenay, QC G7H 2B1, Canada)

  • Lakhdar Bessissa

    (Materials Science and Informatics Laboratory (MSIL), University Ziane Achour of Djelfa, BP 3117 Route of Moudjbara, Djelfa 17000, Algeria)

  • Sid Ahmed Bessedik

    (Laboratory for Analysis and Control of Energy Systems and Electrical Systems (LACOSERE), Laghouat University, Laghouat 03000, Algeria)

  • Oscar Henry Arroyo-Fernandez

    (Research Institute d’Hydro-Québec, 1800 Boulevard Lionel-Boulet, Varennes, QC J3X 1S1, Canada)

  • Jocelyn Jalbert

    (Research Institute d’Hydro-Québec, 1800 Boulevard Lionel-Boulet, Varennes, QC J3X 1S1, Canada)

Abstract

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization ( DP V ) and tensile index ( Tidx )) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DP V and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

Suggested Citation

  • Ahmed Sayadi & Djillali Mahi & Issouf Fofana & Lakhdar Bessissa & Sid Ahmed Bessedik & Oscar Henry Arroyo-Fernandez & Jocelyn Jalbert, 2023. "Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging," Energies, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6455-:d:1234374
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6455/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/18/6455/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bonginkosi A. Thango & Pitshou N. Bokoro, 2022. "Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network," Energies, MDPI, vol. 15(12), pages 1-12, June.
    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. Fan Cai & Yuesong Jiang & Wanqing Song & Kai-Hung Lu & Tongbo Zhu, 2024. "Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm," Energies, MDPI, vol. 17(6), pages 1-15, March.
    2. 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.
    3. Georgi Ivanov & Anelia Spasova & Valentin Mateev & Iliana Marinova, 2023. "Applied Complex Diagnostics and Monitoring of Special Power Transformers," Energies, MDPI, vol. 16(5), pages 1-24, February.
    4. Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.

    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:16:y:2023:i:18:p:6455-:d:1234374. 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.