Circulating Current Minimization in High-Frequency AC Power Distribution Architecture With Multiple Inverter Modules Operated in Parallel
Bibliographic record
Abstract
The control issue of multiple inverter modules operated in parallel is investigated for high-frequency alternative current (HFAC) power distribution architectures, where multiple high-frequency resonant inverters are connected in parallel to the high-frequency high-voltage low-current (HVLC) AC bus, to feed a number of point-of-use power supplies. The circulating current in the multiple inverter system is analyzed first. A novel control scheme is proposed based on the active and reactive current decomposition concept. In the proposed control, there are two loops: 1) the current sharing control loop and 2) the voltage feedback control loop. For the current sharing loop, the active current and reactive current are controlled separately. It is shown that the minimization of the circulating current can be achieved if both the active current and reactive current of the equivalent load are evenly distributed among activated inverter modules. This control method is superior to the scalar control where only the magnitudes of the currents are controlled. Performance is verified with both simulations and experiments on a prototype HFAC power distribution system where two two-stage resonant inverter modules of 500 kHz and 100 W are connected in parallel through small connection inductors to the 500-kHz 28-V rms HFAC bus.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".