Design and Analysis of Sen Transformer Using FEM and No Load Loss Calculation
Bibliographic record
Abstract
The "Sen Transformer (ST)" was introduced by Dr. Kalyan Sen for controlling the power flow in transmission line.The ST provides true need of optimal power flow control that is characterized as a reliable, efficient and least expensive device.The primary of ST is connected in shunt with the transmission line, which is energized transformer core continuously.As ST is connected with the transmission line for 24 hours, a constant no load loss of energy occurs in ST.Due to an expressive loss of energy and an unfortunate influence of losses on the performance of ST, significant core loss (no load loss) is examined as a judgmental factor.Not only with pure input supply, but also with harmonics in supply and even in over excited condition, the behavior of ST should be examined.Finite Element Method (FEM) has been used over here to simulate core loss of ST and investigate behavior of flux density, magnetic field, core losses etc. with sinusoidal as well as non-sinusoidal input sources.Two different structures of ST are considered, which is used for distribution line, in proposed work and the effects of non sinusoidal voltage on no load loss have been investigated and compared for both structures using Ansys Maxwell.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".