THE SAFETY TECHNOLOGY OF ON-LINE DISSOLVED GAS ANALYSIS ON OIL FILLED POWER TRANSFORMERS
Bibliographic record
Abstract
The present paper gives the results of a survey of several typical on-line Dissolved Gas Analysis (DGA) monitors. The purpose is to have an in-depth understanding of the status quo and development trends of the current DGA techniques. The current on-line DGA monitoring techniques can be divided into the following key categories: separation of fault gases from oil, detecting and measuring the fault gases in situ and remote data transmission and fault diagnosis. The characteristics of each DGA monitor are reviewed. The differences range from sampling system to detecting method, and the solutions include direct on-line automatic monitoring and portable manual devices. GP-100 gas extractor produced by Morgan Schaffer Co. in Canada, curtails the additional oil circulating system and realizes collecting gases automatically and passively in situ. The techniques of simultaneous multi-gas detection are developing continuously. Solid sensors, in situ gas chromatography, and Fast Fourier Infrared Transformation techniques are all promising. Solid sensors are cheap, but easy to be poisoned and cross sensitive, and their present technique cannot yet satisfy the practical requirements. In situ gas chromatography, a technique that has been successfully applied to kinds of circumstances by several companies, is still difficult to step out due to the inconvenient exchange of consumable gases and the short lifetime chromatography column. From a technique perspective, FTIR is noninvasive, without cross-interference and uses no consumable gas, with the detection limit compatible with laboratory DGA analysis and stable accuracy, can also measure the water content, is an ideal monitor except manufacture expense and inability on measuring symmetric molecules such as hydrogen, oxygen, and nitrogen. Should these techniques be properly decomposed and reorganized, a new generation of on-line multi-gas analyzer will be shaped in characteristics of not only stable and reliable, but also cheap and practical for realizing condition-based-monitoring of power transformers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".