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

Mohammed Alenezi (), Fatih Anayi, Michael Packianather and Mokhtar Shouran
Additional contact information
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

Energies, 2025, vol. 18, issue 8, 1-23

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.

Keywords: power transformer protection; fault detection; machine learning classifiers; early fault diagnosis; optimization algorithms; feature extraction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/8/2024/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/8/2024/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:8:p:2024-:d:1635130

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().