<![CDATA[This study aims to evaluate transformer insulating oil degradation and post-purification recovery by integrating conventional Dissolved Gas Analysis (DGA) interpretation methods with a fuzzy-logic-based diagnostic framework. The research was conducted as an in-depth case study on Transformer 1 at the Cipinang Gas Insulated Substation (GIS), where repeated DGA measurements indicated progressive thermal stress prior to maintenance intervention. Oil samples were collected at multiple observation points before and after purification and analyzed using established DGA techniques, including Total Dissolved Combustible Gas (TDCG), Roger’s Ratio, Doernenburg Ratio, and the Key Gas Method. These outputs were then incorporated into a fuzzy inference system (FIS) developed in MATLAB to generate a unified oil-condition index. The results show that before purification, elevated concentrations of C₂H₄, C₂H₆, CO, and increasing TDCG values consistently indicated incipient thermal faults. After purification, combustible gas levels and TDCG values declined significantly, shifting the transformer condition to a normal operating state. While conventional ratio-based methods occasionally produced borderline or ambiguous classifications, the fuzzy-logic framework successfully consolidated multiple diagnostic outputs into a single, consistent condition assessment. The study demonstrates that integrating DGA with fuzzy inference enhances diagnostic clarity, improves post-purification evaluation, and supports more reliable transformer maintenance decision-making.]]>
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