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Hybrid Artificial Intelligence Model for Reliable Decision Making in Power Transformer Maintenance Through Performance Index

Author

Listed:
  • Vinícius Faria Costa Mendanha

    (School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • André Pereira Marques

    (Electrical Engineering, Federal Institute of Goiás, Goiânia 74055-110, Brazil)

  • Lucas Santos de Aguiar

    (School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • Juliermy Junio Pacheco dos Santos

    (School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • Álisson Assis Cardoso

    (School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • Cacilda de Jesus Ribeiro

    (School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

Abstract

The preventive maintenance of power transformers is essential to ensure their reliability and is supported by efficient predictive techniques and accurate diagnostics. In this context, the objective of this work is to present a hybrid Artificial Intelligence (AI) model for reliable decision making in transformer maintenance based on performance index monitoring. The innovation lies in the application of Monte Carlo filters to monitor the operational state of transformers combined with a novel clustering strategy. The used methodology includes the development of an algorithm for outlier removal in the historical series of each predictive technique as well as the implementation of stochastic filters to forecast the overall operational condition. The results demonstrate the robustness and effectiveness of the developed model. This work contributes a new AI-based strategy for supporting preventive maintenance decisions, enabling precise and individualized actions for each piece of equipment, with broad applicability to companies in the electrical power sector.

Suggested Citation

  • Vinícius Faria Costa Mendanha & André Pereira Marques & Lucas Santos de Aguiar & Juliermy Junio Pacheco dos Santos & Álisson Assis Cardoso & Cacilda de Jesus Ribeiro, 2025. "Hybrid Artificial Intelligence Model for Reliable Decision Making in Power Transformer Maintenance Through Performance Index," Energies, MDPI, vol. 18(18), pages 1-27, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4924-:d:1750701
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    References listed on IDEAS

    as
    1. Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    2. Mohammed El Amine Senoussaoui & Mostefa Brahami & Issouf Fofana, 2021. "Transformer Oil Quality Assessment Using Random Forest with Feature Engineering," Energies, MDPI, vol. 14(7), pages 1-15, March.
    3. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    4. Engin Baker & Secil Varbak Nese & Erkan Dursun, 2023. "Hybrid Condition Monitoring System for Power Transformer Fault Diagnosis," Energies, MDPI, vol. 16(3), pages 1-11, January.
    5. 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.
    6. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, June.
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