A Review and Comparison of Solid, Multi-Strands and Litz Style PCB Winding
Bibliographic record
Abstract
At high frequency, AC resistance of a printed circuit board (PCB) winding becomes important and accounts for a large proportion of planar transformer losses. The winding is then influenced by both skin and proximity phenomenon, which makes the current distribution uneven resulting in an increased resistance. The study of improving AC resistance of a PCB winding has been tackled by many researchers. However, the lack of an overview and comparison among improvements has made it difficult to apply those methods to a specific winding. To overcome the above limitations, this paper investigates the pros and cons of three popular AC resistance optimizing methods: optimizing track width of a solid PCB winding, using multi-strands and using Litz style PCB winding. To verify the theoretical analysis, a total of 12 PCBs are simulated by finite element (FEM) and tested in the laboratory. Five criteria are analyzed, including skin resistance, proximity resistance, AC to DC ratio, total AC resistance and complexity are taken into consideration. The results of this study show that optimizing track width method has a significant improvement on AC resistance while the use of Litz PCB is effective for applications that need stable AC resistance in a wide frequency range. The use of parallel strands winding should be carefully considered as there is not significant benefit in both reducing the AC resistance and AC to DC ratio.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".