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

Muhammad Akmal A. Putra (), Suwarno and Rahman Azis Prasojo
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Muhammad Akmal A. Putra: School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, Indonesia
Suwarno: School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, Indonesia
Rahman Azis Prasojo: Department of Electrical Engineering, Politeknik Negeri Malang, Malang 65141, Indonesia

Energies, 2025, vol. 18, issue 9, 1-44

Abstract: 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 performance. This study investigates the application of oversampling techniques to enhance ML model accuracy in predicting the Health Index of transformers. A dataset comprising 3850 transformer tests collected from utilities across Indonesia was used. Key parameters, including oil quality, dissolved gas analysis, and paper condition factors, were employed as inputs for ML modeling. To address the class imbalance, various oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, SMOTE-Tomek, and SMOTE-ENN, were implemented and compared. This study explores the impact of these techniques on model performance, focusing on classification accuracy, precision, recall, and F1-score. The results reveal that all SMOTE-based methods improved model performance, with SMOTE-ENN yielding the best outcomes. It significantly reduced classification errors, particularly for minority classes, ensuring better predictive reliability. These findings underscore the importance of advanced oversampling techniques in improving transformer diagnostics. By effectively addressing the challenges posed by imbalanced datasets, this research provides a robust framework for applying ML in transformer condition monitoring and other domains with similar data constraints.

Keywords: transformer diagnostics; Health Index; machine learning; imbalance dataset; condition assessment; Synthetic Minority Oversampling Technique (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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