IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i14p3755-d1702318.html
   My bibliography  Save this article

Optimization of Electric Transformer Operation Through Load Estimation Based on the K-Means Algorithm

Author

Listed:
  • Pedro Torres-Bermeo

    (Centro de Investigación MIST, Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador)

  • José Varela-Aldás

    (Centro de Investigación MIST, Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador)

  • Kevin López-Eugenio

    (Centro de Investigación MIST, Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador)

  • Nancy Velasco

    (Facultad de Informática y Electrónica, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba 060155, Ecuador)

  • Guillermo Palacios-Navarro

    (Department of Electronic Engineering and Communications, University of Zaragoza, 44003 Teruel, Spain)

Abstract

This study presents an innovative methodology to optimize the operation of distribution transformers through the estimation of hourly load curves, aimed at minimizing technical losses due to oversizing, particularly in systems lacking advanced metering infrastructure. The proposed approach combines clustering techniques, K-Means with DTW, to identify representative daily consumption patterns and a supervised model based on LightGBM to estimate hourly load curves for unmetered transformers, using customer characteristics as input. These estimated curves are integrated into a process that calculates technical losses, both no-load and load losses, for different transformer sizes, selecting the optimal rating that minimizes losses without compromising demand. Empirical validation showed accuracy levels of 95.6%, 95.29%, and 98.14% at an individual transformer, feeder, and a complete electrical system with 16,864 transformers, respectively. The application of the methodology to a real distribution system revealed a potential annual energy savings of 3004 MWh, equivalent to an estimated economic reduction of 150,238 USD.

Suggested Citation

  • Pedro Torres-Bermeo & José Varela-Aldás & Kevin López-Eugenio & Nancy Velasco & Guillermo Palacios-Navarro, 2025. "Optimization of Electric Transformer Operation Through Load Estimation Based on the K-Means Algorithm," Energies, MDPI, vol. 18(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3755-:d:1702318
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/14/3755/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/14/3755/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ayman Agha & Hani Attar & Ashish Kr. Luhach, 2021. "Optimized Economic Loading of Distribution Transformers Using Minimum Energy Loss Computing," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, December.
    2. Zikuo Dai & Kejian Shi & Yidong Zhu & Xinyu Zhang & Yanhong Luo, 2023. "Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load," Energies, MDPI, vol. 16(11), pages 1-19, May.
    3. Pedro Torres-Bermeo & Kevin López-Eugenio & Carolina Del-Valle-Soto & Guillermo Palacios-Navarro & José Varela-Aldás, 2025. "Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models," Energies, MDPI, vol. 18(7), pages 1-24, April.
    4. Tomasz Piotrowski & Dorota Markowska, 2025. "Carbon Footprint of Power Transformers Evaluated Through Life Cycle Analysis," Energies, MDPI, vol. 18(6), pages 1-20, March.
    5. Mantas Plienis & Tomas Deveikis & Audrius Jonaitis & Saulius Gudžius & Inga Konstantinavičiūtė & Donata Putnaitė, 2023. "Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis," Energies, MDPI, vol. 16(23), pages 1-16, November.
    6. Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, 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. Pedro Torres-Bermeo & Kevin López-Eugenio & Carolina Del-Valle-Soto & Guillermo Palacios-Navarro & José Varela-Aldás, 2025. "Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models," Energies, MDPI, vol. 18(7), pages 1-24, April.
    2. Nassim A. Iqteit & Khalid Yahya & Firas M. Makahleh & Hani Attar & Ayman Amer & Ahmed Amin Ahmed Solyman & Ahmad Qudaimat & Khaled Tamizi, 2022. "Simple Mathematical and Simulink Model of Stepper Motor," Energies, MDPI, vol. 15(17), pages 1-14, August.
    3. Tariq Kamal & Syed Zulqadar Hassan, 2023. "Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”," Energies, MDPI, vol. 16(22), pages 1-4, November.
    4. Baitong Zhai & Dongsheng Yang & Bowen Zhou & Guangdi Li, 2024. "Distribution System State Estimation Based on Power Flow-Guided GraphSAGE," Energies, MDPI, vol. 17(17), pages 1-15, August.
    5. Grzegorz Hołdyński & Zbigniew Skibko & Wojciech Walendziuk, 2024. "Power and Energy Losses in Medium-Voltage Power Grids as a Function of Current Asymmetry—An Example from Poland," Energies, MDPI, vol. 17(15), pages 1-18, July.
    6. Mantas Plienis & Tomas Deveikis & Audrius Jonaitis & Saulius Gudžius & Inga Konstantinavičiūtė & Donata Putnaitė, 2023. "Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis," Energies, MDPI, vol. 16(23), pages 1-16, November.
    7. Guanxun Diao & Heli Ni & Wenrong Si & Yingjie Gu & Jian Yang, 2025. "Multi-Physics Numerical Research in Oil-Immersed Three-Phase Transformer Under Load Unbalance," Energies, MDPI, vol. 18(5), pages 1-34, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:14:p:3755-:d:1702318. 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.