Impacts of Various Representations of Core Saturation Curve on Ferroresonance Behavior of Transformers
Bibliographic record
Abstract
This paper investigates and compares the impacts of two widely used types of the transformer core representation, i.e. piecewise linear magnetization characteristic and two-term polynomial-based saturation curve, on the ferroresonance behaviors of a power transformer. The polynomial-based saturation curve is implemented in the EMTP-RV environment, using Dynamic Link Library (DLL) programming feature. Such an implementation participates in the solution of the equation set of the system and results in true nonlinear solutions of the ferroresonance phenomenon. The simulation results indicate that the ferroresonance behaviors of the transformer under study, based on the piecewise linear and the polynomial saturation characteristics, are significantly different. The two-term polynomial has limited flexibility to represent the saturation characteristic of the transformer around the knee point and in both linear and saturation regions. Although ferroresonance behavior of a transformer highly depends on the characteristic of the core above the rated excitation level, an inaccurate representation of the characteristic in the linear part can result in erroneous ferroresonance conditions.
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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 | 0.006 |
| 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 | 0.000 | 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".