A New Inductive Power Transfer Topology Using Direct AC–AC Converter With Active Source Current Waveshaping
Bibliographic record
Abstract
Generally, in wireless inductive power transfer (IPT) system, the power is processed through multiple power transfer stages and this leads to lower efficiency and higher cost of the system. Recent research shows that the use of a direct ac–ac converter in an IPT system compensates these limitations significantly. However, one of the major challenges of the IPT circuit with direct ac–ac converter is to achieve multiple control goals through a single converter. These include load power requirement, maintaining high-quality source current and achieving soft switching of inverter switches, etc. In the existing literatures, the research is more focused on meeting load power requirement and soft switching of inverter switches. The major focus of this paper is to propose, analyze, and control a new IPT power converter topology using current-fed direct ac–ac converter. Compare with existing buck derived, i.e., voltage source ac–ac converter topologies, the proposed topology is boost derived; therefore, active source current waveshaping is easily obtained. The complete control is carried out through two loops, where the outer output current loop ensures load requirements and inner loop maintains the high-quality grid current. The detail of steady-state and dynamic analysis and design procedure of the converter is presented. Experimental results obtained from a 1.2-kW lab-build prototype are included to verify the analysis and proposed control.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".