Improving Transformer Health Index Prediction Performance Using Machine Learning Algorithms with a Synthetic Minority Oversampling Technique

Improving Transformer Health Index Prediction Performance Using Machine Learning Algorithms with a Synthetic Minority Oversampling Technique

Machine learning (ML) has emerged as a powerful tool in transformer condition assessment, enabling more accurate diagnostics by leveraging historical test data. However, imbalanced datasets, often characterized by limited samples in poor transformer conditions, pose significant challenges to model p...

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Bibliographic Details
Main Authors: Muhammad Akmal A. Putra, Suwarno, Rahman Azis Prasojo
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Energies
Subjects:
transformer diagnostics
Health Index
machine learning
imbalance dataset
condition assessment
Synthetic Minority Oversampling Technique
Online Access:https://www.mdpi.com/1996-1073/18/9/2364
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