Analysis of Leakage Impedance Variations Due to Cross-Sectional Area Changes in High Voltage Sections
Bibliographic record
Abstract
Transformers are crucial devices in electrical power systems, playing a key role in voltage regulation, power transmission, and distribution. Leakage impedance is one of the main factors influencing the transformer’s performance. Leakage impedance affects the efficiency and stability of the power transformers. This paper focuses on the numerical estimation for the calculation of leakage impedance with three sections in the primary winding (high-voltage winding) and a single section in the secondary winding (low-voltage winding). This paper aims to comprehensively examine the variations that arise from the different cross-sectional areas of the various sections of the high-voltage winding, as well as how these variations affect the transformer’s overall leakage impedance. This study focuses on examining the effect of different cross-section areas of the high-voltage winding on leakage impedance. Finite element analysis is used to measure and evaluate the leakage impedance of the transformer under different configurations. This study examines the impact of cross-sectional area variations on the transformer’s leakage impedance, highlighting the crucial roles of impedance and inductance.
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 | 0.001 | 0.001 |
| 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".