IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i8p2024-d1635130.html
   My bibliography  Save this article

A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO Algorithms

Author

Listed:
  • Mohammed Alenezi

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Fatih Anayi

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Michael Packianather

    (High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Mokhtar Shouran

    (Libyan Center for Engineering Research and Information Technology, Bani Walid 00218, Libya)

Abstract

The early detection of faults in power transformers is crucial for ensuring operational reliability and minimizing system disruptions. This study introduces a novel machine learning framework that integrates Particle Swarm Optimization (PSO) and Dwarf Mongoose Optimization (DMO) algorithms for feature selection and hyperparameter tuning, combined with advanced classifiers such as Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVM). A 5-fold cross-validation approach was employed to ensure a robust performance evaluation. Feature extraction was performed using both Discrete Wavelet Decomposition (DWD) and Matching Pursuit (MP), providing a comprehensive representation of the dataset comprising 2400 samples and 41 extracted features. Experimental validation demonstrated the efficacy of the proposed framework. The PSO-optimized RF model achieved the highest accuracy of 97.71%, with a precision of 98.02% and an F1 score of 98.63%, followed by the PSO-DT model with a 95.00% accuracy. Similarly, the DMO-optimized RF model recorded an accuracy of 98.33%, with a precision of 98.80% and an F1 score of 99.04%, outperforming other DMO-based classifiers. This novel framework demonstrates significant advancements in transformer protection by enabling accurate and early fault detection, thereby enhancing the reliability and safety of power systems.

Suggested Citation

  • Mohammed Alenezi & Fatih Anayi & Michael Packianather & Mokhtar Shouran, 2025. "A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO Algorithms," Energies, MDPI, vol. 18(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2024-:d:1635130
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/8/2024/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/8/2024/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed Alenezi & Fatih Anayi & Michael Packianather & Mokhtar Shouran, 2024. "Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection," Sustainability, MDPI, vol. 16(23), pages 1-23, December.
    2. Al Dawsari, Saleh & Anayi, Fatih & Packianather, Michael, 2024. "Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm," Energy, Elsevier, vol. 313(C).
    3. Tamer Khatib & Gazi Arar, 2020. "Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, 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. Meysam Beheshti Asl & Issouf Fofana & Fethi Meghnefi & Youssouf Brahami & Joao Pedro Da Costa Souza, 2025. "A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches," Energies, MDPI, vol. 18(5), pages 1-38, March.
    2. Mohammed Alenezi & Fatih Anayi & Michael Packianather & Mokhtar Shouran, 2024. "Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection," Sustainability, MDPI, vol. 16(23), pages 1-23, December.

    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:8:p:2024-:d:1635130. 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.