A comprehensive condition monitoring solution for the transformer
Bibliographic record
Abstract
In most aging transformers, windings failures are caused by the deterioration of the insulation, leading to arcing and short circuits. Vibrations in a transformer are generated by different forces appearing in the core and winding during the operation. Winding vibrations result from the electromagnetic forces that are generated by the interaction of the current in a winding with leakage flux. These forces are proportional to the square of the load current, and therefore vibration is always there. VibroFibre™ technology was invented by QPS to monitor the vibration of the end windings. It works according to optical interference, i.e., when subjected to vibration, the signal reflected off the sensor is interpreted as intensity changes resulting from the change in the length of a fiber Bragg grating-based cavity. However, as temperature rises, the fringes also move making it necessary for other control measures to track this temperature rise. These counter measures effectively constitute indirect temperature measurement during this vibration monitoring. In other words, one single sensor can give two critical parameters The same technology is now adapted to be applied to diagnose transformers. The twin cavity sensor is packaged to measure trace moisture in oil. This moisture sensor has built in temperature compensation as well as direct temperature read out. This paper will describe the theoretical basis of this twin cavity sensor. In addition, we will also describe the fiber laser sensor for PD measurement. Because of the fact these PD sensors are to be mounted on the transformer wall. QPS continues to work on a miniature acoustic chamber .The paper will also discuss conceptually the algorithm behind the location of PD events in the context of a four wall transformer. Finally we will discuss in details the moisture sensor cable of reading moisture down to 1ppm with built in temperature compensation. Actually this moisture sensor is capable to give direct readout of the oil temperature which is the first in the world.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".