A high efficiency high frequency resonant inverter for high frequency ac power distribution architectures
Bibliographic record
Abstract
A high efficiency two-stage resonant inverter with effective control of both the magnitude and phase angle of the output voltage was proposed in this paper for high frequency AC (HFAC) power distribution applications, where a number of resonant inverters need to be paralleled. In order to parallel multiple resonant inverters of the same operation frequency, each inverter module need independent control of the phase angle and magnitude. It is also desirable that the output voltage has very low total harmonics distortion, as well as high efficiency over wide input and load ranges. The proposed resonant inverter consists of two stages. The first stage is a two-switch DC/DC converter with zero voltage transition, and the second stage is a half-bridge resonant DC/AC inverter with fixed duty ratio. A series-parallel resonant tank is used to achieve high waveform quality of the output voltage. The output voltage of the inverter can be regulated through the pulse width modulation of the duty ratio of the first stage. The phase angle of the resonant inverter can be regulated through a pulse phase modulation control of the second stage. The proposed resonant inverter has the advantages of better waveform quality, and wide range of input and load variations for soft-switching, independent control of the phase angle and magnitude, making it an attractive selection for applications where a number of resonant inverters need to be placed between the DC bus and HFAC bus, and a number of distributed loads are connected to the HFAC bus. The performance is verified with both simulation and experiments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".