Multi-Turn Modeling of a Power Transformer under an Inter-Turn Short-Circuit Fault
Bibliographic record
Abstract
This paper presents a new method for modeling the steady-state behavior of the currents transmitted between the windings of a transformer subjected to an inter-turn short-circuit fault (ITSC). ITSC is one of the most frequent and most damaging faults in magnetically coupled circuits, which occur in power transformers. Coupled Electromagnetic Circuit (CEMC) Model modeling the reasoning and relationships describing the operating principle of a power transformer through the electrical and magnetic parameters defined by the self and mutual inductances that influence the voltages and currents transmitted between the transformer windings. The representation of the state variable equations of a proposed model of a three-phase multi-turn transformer in healthy mode (no fault) and in degraded mode (with inter-turn short-circuit faults of 10%, 20% and 30%) has been implemented using a program inserted in MATLAB software. The simulations illustrate the evaluation of the primary and secondary currents as well as the voltage drop across a load, and the accuracy of the state model based on the coupled circuits was validated. The results obtained can provide a basis for the design of the short circuit of the multi-turn transformer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".