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All Journal International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Kurniawan, Wahyu Citra
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Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Medicine & Pharmacology

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Optimizing Power Transformer Failure Identification: A Multi-Method Framework Based on Normalized Energy Intensity According to IEEE C57.104-2019 Standards Adapted to Indonesian Power Transformer Characteristics Kurniawan, Wahyu Citra; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.121

Abstract

This research develops and validates a multi-method diagnostic framework by integrating Normalized Energy Intensity (NEI) parameters according to IEEE C57.104-2019 standards adapted for Indonesian power transformer populations. Analysis of 1525 DGA samples from PLN Indonesia transformers reveals significant differences in percentile thresholds compared to North American standards. Using unadapted North American thresholds categorized 68.4% of transformers as critical (DGA Status 3), while adapted thresholds reduced this to 25.1%. Duval Triangle 1 identified Discharge of Low Energy (D1) as the dominant failure type (35.4%), while Duval Pentagon 1 showed dominance of Discharge of High Energy (D2) (39.4%), and Duval Pentagon 2 identified Stray gassing (S) (27.6%) and Overheating without paper carbonization (O) (22.3%). Pearson correlation analysis on transformers with O₂/N₂ ratio ≤ 0.2 showed strong correlations between NEI Oil with ethylene (R = 0.877) and methane (R = 0.845), while NEI Paper strongly correlated with carbon monoxide (R = 0.934). NEI Oil combined with hydrocarbon gas concentrations provided more consistent patterns with multi-method fault identification than NEI Paper. Multi-method validation proved absolute gas concentration methods more reliable than gas ratio methods. This framework improves maintenance efficiency by reducing false alarms and optimizing preventive strategies.
Co-Authors Budi Sudiarto