Bees Algorithm and PSO-Optimized Hybrid Models for Accurate Power Transformer Fault Diagnosis: A Real-World Case Study

Mohammed Alenezi (), Jabir Massoud, Tarek Ghomeed and Mokhtar Shouran
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Mohammed Alenezi: School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Jabir Massoud: School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Tarek Ghomeed: College of Electronic Technology, Bani Walid P.O. Box 38645, Libya
Mokhtar Shouran: College of Electronic Technology, Bani Walid P.O. Box 38645, Libya

Energies, 2025, vol. 18, issue 22, 1-27

Abstract: This paper introduces an intelligent fault-diagnosis framework for power transformers that integrates hybrid machine-learning models with nature-inspired optimization. Current signals were acquired from a laboratory-scale three-phase transformer under both healthy and various fault conditions. A suite of 41 discriminative features was engineered from time–frequency and sparse representations generated via Discrete Wavelet Transform (DWT) and Matching Pursuit (MP). The resulting dataset of 2400 labeled segments was used to develop four hybrid models, PSO-SVM, PSO-RF, BA-SVM, and BA-RF, wherein Particle Swarm Optimization (PSO) and the Bees Algorithm (BA) served as wrapper optimizers for simultaneous feature selection and hyperparameter tuning. Rigorous evaluation with 5-fold and 10-fold cross-validation demonstrated the superior performance of Random Forest-based models, with the BA-RF hybrid achieving peak performance (98.33% accuracy, 99.09% precision). The results validate the proposed methodology, establishing that the fusion of wavelet- and MP-based feature extraction with metaheuristic optimization constitutes a robust and accurate paradigm for transformer fault diagnosis.

Keywords: power transformers; fault diagnosis; discrete wavelet transform (DWT); metaheuristic optimization; classification algorithms (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
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