Comprehensive Regression Modeling for Evaluating Chemical Properties of Transformer Oil Under Thermal Aging Conditions

This study examines thermal aging effects on transformer oil's acidity and water content using an innovative exponential regression approach. Mineral oil samples were aged at 80°C, 100°C, 120°C, and 140°C for 5000 hours, with properties measured at 500-hour intervals. Results reveal temperaturedependent degradation, showing accelerated deterioration above 100°C. The exponential model achieved strong correlations $\left(R^{2} >90 \%\right)$ for both acidity and water content, demonstrating its effectiveness in predicting oil aging. These findings provide valuable insights for transformer condition assessment and maintenance planning, highlighting the model's potential for practical application in power system reliability management. The research establishes clear relationships between thermal stress, chemical degradation, and measurable oil properties, offering a robust framework for transformer health monitoring.