Multiclass Fault Diagnosis in Power Transformers Using Dissolved Gas Analysis and Grid Search-Optimized Machine Learning

Multiclass Fault Diagnosis in Power Transformers Using Dissolved Gas Analysis and Grid Search-Optimized Machine Learning

Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations...

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Bibliographic Details
Main Authors: Andrew Adewunmi Adekunle, Issouf Fofana, Patrick Picher, Esperanza Mariela Rodriguez-Celis, Oscar Henry Arroyo-Fernandez, Hugo Simard, Marc-André Lavoie
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
Subjects:
power transformer
dissolved gas analysis
diagnostic techniques
physicochemical and dielectric properties
machine learning models
Online Access:https://www.mdpi.com/1996-1073/18/13/3535
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