Improving the Reliability of Dissolved Gas Analysis (DGA) Diagnostics Through Consideration of Measurement Device Uncertainty

During operation, due to an unforeseen circumstance, an in-service transformer could experience some faults, which could be of electrical or thermal origin. Transformers diagnostic and monitoring through dissolved gas analysis (DGAs) is an imperative way for sustainable and reliable power distribution. This method is used in the early detection of faults in liquid-filled power systems. In this contribution, a novel approach integrating uncertainty into DGA for power transformers is proposed. By modifying traditional empirical methods like Duval’s Triangle and Duval’s Pentagon to include uncertainty, this study demonstrates how these modifications affect fault diagnostics. The results indicate that incorporating the gas chromatograph’s uncertainty enhances diagnostic accuracy for IEC, Rogers, and Doernenburg methods, improving effective fault detection. Additionally, artificial intelligence (AI) models such as random forest (RF), MLP, and support vector machine (SVM), trained with probabilities (PRs) derived from the modified methods, show significant improvements in diagnostic performance. These models demonstrated increased robustness against different levels of uncertainty, achieving 87.76% accuracy for the RF model and 86.73% for the SVM model with a 30% uncertainty level on the IEC TC10 Database. Therefore, the incorporation of the Gas chromatograph’s uncertainty in DGA diagnostic methods appears to be a promising approach to support transformer monitoring and fault diagnostic both for transformer owners and the producer.