Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning

This study addresses the challenge of imbalanced dissolved gas analysis (DGA) data in transformer failure classification by assessing the impact of data-level balancing techniques on machine learning performance. Five data-level strategies - Random Under-Sampling (RUS), Edited Nearest Neighbors (ENN...

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書目詳細資料
發表在:ENERGY REPORTS
Main Authors: Azmi, Putri Azmira R.; Yusoff, Marina; Sallehud-din, Mohamad Taufik Mohd
格式: Article
語言:English
出版: ELSEVIER 2025
主題:
在線閱讀:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386454100001
實物特徵
總結:This study addresses the challenge of imbalanced dissolved gas analysis (DGA) data in transformer failure classification by assessing the impact of data-level balancing techniques on machine learning performance. Five data-level strategies - Random Under-Sampling (RUS), Edited Nearest Neighbors (ENN), NearMiss (NM), Random Over-Sampling (ROS), and ADASYN - were applied to balance the dataset and improve classification outcomes. The dataset includes key gas concentrations (H2, CH4, C2H6, C2H4, and C2H2) and a target defect variable (act). Three machine learning algorithms - Support Vector Machine, Decision Tree, and Random Forest - were tested, with results showing that ENN combined with SVM achieved the highest classification performance: 88% accuracy, 89.89% precision, 88.00% recall, 86.64% F1-score, and a runtime of 0.21 s. This approach demonstrates the effectiveness of data-level techniques in improving transformer fault diagnosis, offering a robust path forward for enhancing electrical power system reliability. Future research should refine these techniques and explore their integration with optimized models to enhance the accuracy of the proposed technique.
ISSN:2352-4847
DOI:10.1016/j.egyr.2024.12.006