An Accurate Hysteresis Model for Ferroresonance Analysis of a Transformer
Bibliographic record
Abstract
This paper introduces an accurate transformer core model, using the Preisach theory, to represent the core magnetization characteristic. This modeling approach provides the required precision to match major and minor hysteresis loops of the model with those of the actual transformer core material. Using the proposed model, the ferroresonance phenomenon of a voltage transformer (VT) is simulated and compared to the corresponding experimental results. In addition, the simulated ferroresonance behavior of the VT based on: 1) the Electromagnetic Transients Program (EMTP)-RV hysteric reactor model, 2) the EMTP reactor type-96 model, and 3) a single-valued polynomial as magnetization characteristic, is also deduced and compared with the proposed model and the experimental results. The investigations conclude that the proposed model provides the most accurate results in terms of the VT core losses and the VT voltage waveforms and their peak values during 1) normal operating conditions, 2)ferroresonance transients and jumping from normal to ferroresonance operating conditions, and 3) ferroresonance steady-state conditions. Furthermore, higher accuracy of the proposed model in representing the hysteresis loop provides the highest and the most accurate bifurcation point and the transient loss when compared with other models. This paper also concludes that major and minor hysteresis loops must be represented in the core model to accurately simulate ferroresonance phenomenon of the VT.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".