Work

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

Year: 2025

Type: article

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.

Source: Energies

DOI: 10.3390/en18225964

Authors: Mohammed Alenezi, Jabir Massoud, Tarek Ghomeed, Mokhtar Shouran

Institutions: Cardiff University, College of Electronic Technology Bani Walid

Language: English

Topic: Power Transformer Diagnostics and Insulation

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137