Improving the Reliability of Dissolved Gas Analysis (DGA) Diagnostics Through Consideration of Measurement Device Uncertainty
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".