New magnetizing inrush restraining algorithm for power transformer protection
Bibliographic record
Abstract
Large power transformers belong to a class of vital and very expensive components in electric power systems. Accordingly, high demands are imposed on power transformer protective relays. The operating conditions of transformer protection, however, do not make the relaying task easy. Protection of large power transformers is one of the most challenging problems in the area of power system relaying. Magnetizing inrush inhibit is one the issues. Traditional second harmonic restraining technique may face security problems as the level of the second harmonic may drop below the reasonable threshold setting (around 20%) permanently or for several power system cycles during magnetizing inrush conditions. This is particularly true for modern transformers with magnetic cores built with improved materials, but it has a bearing upon old designs as well. Numerical relays capable of performing sophisticated signal processing functions enable the relay designer to re-visit the classical protection principles and enhance the relay performance, facilitating faster, more secure and dependable protection for power transformers. A new magnetizing inrush restraining technique presented in this paper uses the angular relationship between the first and second harmonics of the differential current. Thus, the technique adds a new dimension to the traditional approach that measures the magnitude ratio only between the fundamental frequency component and the second harmonic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".