Performance of various magnetic core models in comparison with the laboratory test results of a ferroresonance test on a 33 kV voltage transformer
Bibliographic record
Abstract
Ferroresonance is a nonlinear phenomenon and occurs in all voltage levels of power systems. One of the main elements of a ferroresonance circuit is saturable inductance. Existing electromagnetic transient programs such as EMTP present various models of magnetization characteristics of saturable cores. However, to study ferroresonance phenomenon, many papers have used single-valued characteristics for magnetic cores and ignored hysteresis effects. In this paper, some modeling methods of magnetic core characteristics are reviewed and the results of a ferroresonance test on a 33 kV voltage transformer are presented. Using the results of the voltage transformer no-load test, the magnetization characteristic is obtained and the magnetic core is modeled by various methods. The comparison between the experimental and simulation results shows that in analysis of some ferroresonance cases, the aspect of inherent variation of inductance in hysteresis loops is more important than the power loss introduced by these loops. Therefore, in such cases, the core characteristic must be represented by a hysteretic model. Otherwise, the simulation results may present significant errors.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".