Impacts of Transformer Core Hysteresis Formation on Stability Domain of Ferroresonance Modes
Bibliographic record
Abstract
This paper investigates impacts of various formations of hysteresis on the stability domain of ferroresonance modes of a voltage transformer (VT). Based on four different hysteretic and two single-valued polynomial models, ferroresonance behaviors of the VT are studied. The hysteretic models are developed based on the Preisach theory. The first hysteretic model accurately duplicates the measured hysteresis loops of the VT in a wide variation range of the core excitation level. The other three hysteretic models represent different hysteresis loop formations. The polynomial models are based on identical single-valued polynomial magnetization characteristics but represent different core loss resistances (i.e., constant and dynamically varying core loss resistances, respectively). All of the models represent the same core losses as the measured value to investigate impacts of hysteresis loop formations on ferroresonance modes, independent of the corresponding hysteresis losses. The studies are conducted in time domain in the PSCAD/EMTDC software environment. The studies indicate that the VT model, which duplicates the measured hysteresis loops and the measured core loss over a wide range of excitation levels, results in more ferroresonance modes, expanded stability domains, and higher overvoltages. This paper concludes that formation of hysteresis is an independent factor which significantly impacts the ferroresonance phenomenon. Not only is it a single-valued magnetization characteristic but also a generic hysteretic characteristic; if it is not accurately constructed based on the measured core hysteresis data, it can result in significant error in determining the ferroresonance overvoltages and stability domains of the ferroresonance modes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".