Monitoring of power transformers using thermal model and permission time of overload
Bibliographic record
Abstract
This paper presents the problem of increasing the reliability of electricity supply to consumers. Uninterrupted power supply to electricity consumers depends on the reliability of power supply system in general and power transformers in particular, the accident rate of which is quite high. The causes of the problem are the location of transformer substations at a considerable distance from the service centers, their spreading out over a large area, missing information about the current modes of their operation and so on. One of the ways to solve this problem is development and implementation a system for continuous diagnostics of power transformers. Failure analysis of power transformer based on fault tree is considered, the diagnostic parameters are determined. The insulation wear rate and permission operating time under overload have been defined with help of equivalent heat circuit. It is proposed to use a permission time as a parameter to diagnose the operation mode and increase the efficiency of maintenance of substations through remote monitoring based on the global service mobile (GSM) network. Remote diagnostics allows to receive an information about emergency situation timely. It helps to reduce operating costs, to ensure the reliability and quality of electricity supply for consumers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".