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

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

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 featu...

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Bibliographic Details
Main Authors: Mohammed Alenezi, Fatih Anayi, Michael Packianather, Mokhtar Shouran
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Energies
Subjects:
power transformer protection
fault detection
machine learning classifiers
early fault diagnosis
optimization algorithms
feature extraction
Online Access:https://www.mdpi.com/1996-1073/18/8/2024
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