IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8716-d978428.html
   My bibliography  Save this article

A Real-Time Method to Estimate the Operational Condition of Distribution Transformers

Author

Listed:
  • Leandro José Duarte

    (Smart Grids Laboratory (LRI), Federal University of Uberlândia, Uberlândia 38408-100, Brazil)

  • Alan Petrônio Pinheiro

    (Smart Grids Laboratory (LRI), Federal University of Uberlândia, Uberlândia 38408-100, Brazil)

  • Daniel Oliveira Ferreira

    (Smart Grids Laboratory (LRI), Federal University of Uberlândia, Uberlândia 38408-100, Brazil)

Abstract

In this article, an unsupervised learning method is presented with the objective of modeling, in real-time, the main operating modes (OM) of distribution transformers. This model is then used to assess the operational condition through use of two tools: the operation map and the health index. This approach allows, mainly, for a reduction in the need for the interpretation of results by specialists. The method used the concepts of k-nearest neighbors (k-NN) and Gaussian mixture model (GMM) clustering to identify and update the main OMs and characterize these through operating mode clusters (OMC). The evaluation of the method was performed using data from a case study of almost one year in duration, along with five in-service distribution transformers. The model was able to synthesize 11 magnitudes measured directly in the transformer into two latent variables using the principal component analysis technique, while preserving on average more than 86% of the information present. The operation map was able to categorize the transformer operation into previously parameterized levels (appropriate, precarious, critical) with errors below 0.26 of standard deviation. In addition, the health index opened the possibility of identifying and quantifying the main abnormal variations in the operating pattern of the transformers.

Suggested Citation

  • Leandro José Duarte & Alan Petrônio Pinheiro & Daniel Oliveira Ferreira, 2022. "A Real-Time Method to Estimate the Operational Condition of Distribution Transformers," Energies, MDPI, vol. 15(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8716-:d:978428
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8716/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8716/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ishan Srivastava & Sunil Bhat & B. V. Surya Vardhan & Neeraj Dhanraj Bokde, 2022. "Fault Detection, Isolation and Service Restoration in Modern Power Distribution Systems: A Review," Energies, MDPI, vol. 15(19), pages 1-26, October.
    2. Akhyurna Swain & Elmouatamid Abdellatif & Ahmed Mousa & Philip W. T. Pong, 2022. "Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments," Energies, MDPI, vol. 15(19), pages 1-37, October.
    3. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.
    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. Muhammad Waseem & Muhammad Adnan Khan & Arman Goudarzi & Shah Fahad & Intisar Ali Sajjad & Pierluigi Siano, 2023. "Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges," Energies, MDPI, vol. 16(2), pages 1-29, January.
    2. Ines Ortega-Fernandez & Francesco Liberati, 2023. "A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-15, January.
    3. Sheeraz Kirmani & Abdul Mazid & Irfan Ahmad Khan & Manaullah Abid, 2022. "A Survey on IoT-Enabled Smart Grids: Technologies, Architectures, Applications, and Challenges," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    4. Angelos Patsidis & Adam Dyśko & Campbell Booth & Anastasios Oulis Rousis & Polyxeni Kalliga & Dimitrios Tzelepis, 2023. "Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods," Energies, MDPI, vol. 16(16), pages 1-19, August.
    5. Vitor Monteiro & Joao L. Afonso, 2023. "The Future of Electrical Power Grids: A Direction Rooted in Power Electronics," Energies, MDPI, vol. 16(13), pages 1-10, June.
    6. Mohammad Kamrul Hasan & AKM Ahasan Habib & Shayla Islam & Mohammed Balfaqih & Khaled M. Alfawaz & Dalbir Singh, 2023. "Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations," Energies, MDPI, vol. 16(3), pages 1-20, January.
    7. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    8. Oscar G. Duarte & Javier A. Rosero & María del Carmen Pegalajar, 2022. "Data Preparation and Visualization of Electricity Consumption for Load Profiling," Energies, MDPI, vol. 15(20), pages 1-30, October.
    9. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    10. Sadeghi, M. & Kalantar, M., 2023. "Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus," Applied Energy, Elsevier, vol. 350(C).
    11. B. V. Surya Vardhan & Mohan Khedkar & Ishan Srivastava & Prajwal Thakre & Neeraj Dhanraj Bokde, 2023. "A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator," Energies, MDPI, vol. 16(3), pages 1-21, 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:jeners:v:15:y:2022:i:22:p:8716-:d:978428. 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.