Research on on-line PD monitoring system for large power transformer
Bibliographic record
Abstract
This paper presents a new set of on-line partial discharge (PD) monitoring system for large power transformer. Four sets of 500 kV power transformers can be monitored automatically at the same time, and then fault warning and localization are made by software. Various methods are used to reject interference in order to increase the monitoring sensitivity of PD. The on-line calibration technique, the pulse injection through capacitive tapping, is applied for measurement of apparent discharge. The earthing fault of transformer was found while the system commissioning. Now this system has been in operation steadily for more than two years. As one of main prevision maintenance testing item, on-line PD monitoring systems are paid more attention by engineers both in China and abroad. Up to now, some on-line systems had already been put in operation in Canada, Japan and China. They have already got many good results and accumulating a lot of good experiences. On base of summarizing forefather's works, a new type of JFY-2 on-line PD monitoring system for large power transformer has been developed by Jilin electric power research institute joint with Tsinghua university.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".