Abstract

The reliable operation of transformers is critical to power system stability, and early fault detection in transformers can significantly reduce the risk of catastrophic failures. Dissolved gas analysis (DGA) is a commonly used method to assess transformer health, as gas concentrations correlate with fault conditions inside the transformer. Methane (CH4), a gas indicative of thermal faults, serves as a key parameter in detecting early signs of fault. This study proposes a linear epsilon support vector regression (SVR) model fine-tuned with Grid Search for predicting methane concentration based on transformer temperature, oil temperature, winding resistance, load factor, service life, and transformer age using grey relational analysis. By predicting methane concentration in ppm, this model supports proactive fault management, optimizing maintenance schedules, and effectively reducing transformer downtime. Comparative results suggest that the proposed SVR model is an effective baseline for improving dissolved gas analysis accuracy, contributing to enhanced transformer diagnostics and preventive maintenance strategies.