Advanced Regression Modeling for Correlating Transformer Oil Electrical Properties with Thermal Aging Trends

In this study, we investigate the impact of thermal aging on crucial electrical properties of mineral oil widely used in transformers, namely the breakdown voltage, dielectric dissipation factor, and resistivity. Our research stands out for its innovative use of an exponential regression model to compre-hensively analyze these properties. Through a meticulous experi-mental approach, we subject transformer oil to a rigorous aging protocol spanning 5000 hours at temperatures of 80°C, 100°C, 120°C, and 140°C. At regular 500-hour intervals, we meticulously evaluate the breakdown voltage, dielectric dissipation factor, and resistivity to track any changes induced by aging. Leveraging advanced regression analysis techniques, particularly employing the exponential model, we accurately characterize the evolving electrical properties of the oil samples. Our findings reveal distinct alterations in breakdown voltage, dielectric dissipation factor, and resistivity following thermal aging, with more signifi-cant degradation observed at higher temperatures. Moreover, our regression analysis closely aligns with experimental results across all samples and characteristics studied, boasting high correlation coefficients. These results affirm the reliability of our model in predicting the electrical properties of aged transformer oil accurately, providing valuable insights for assessing transformer performance.