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

Energies, Год журнала: 2025, Номер 18(8), С. 2024 - 2024

Опубликована: Апрель 15, 2025

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) Dwarf Mongoose (DMO) algorithms feature selection hyperparameter tuning, combined with advanced classifiers such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM). A 5-fold cross-validation approach was employed to ensure robust performance evaluation. Feature extraction performed using both Discrete Wavelet Decomposition (DWD) Matching Pursuit (MP), providing comprehensive representation the dataset comprising 2400 samples 41 extracted features. Experimental validation demonstrated efficacy proposed framework. PSO-optimized RF model achieved highest accuracy 97.71%, precision 98.02% an F1 score 98.63%, followed by PSO-DT 95.00% accuracy. Similarly, DMO-optimized recorded 98.33%, 98.80% 99.04%, outperforming other DMO-based classifiers. demonstrates significant advancements transformer protection enabling accurate fault detection, thereby enhancing safety systems.

Язык: Английский

A comprehensive review of energy security in islanded regions: Challenges, strategies, and sustainable development pathways

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 220, С. 115879 - 115879

Опубликована: Май 30, 2025

Язык: Английский