Very High Efficiency Bridgeless Boost Totem-Pole PFC using Gallium Nitride HEMTs
Bibliographic record
Abstract
Data centers have become the backbone of the global economy. The electricity demand for data centers grew tremendously over the last ten years. U.S. data centers consumed 91 billion kilowatt-hours of electricity in 2013 and are on track to consume 140 billion kilowatt-hours of electricity annually by 2020. AC – DC Rectifiers are the equipment to power the data centers. They convert the AC power from electric utilities into DC power with two power stages. The first stage is the PFC stage that converts the input 220V AC voltage into a 400V DC voltage. The second stage is the DC – DC stage that converts the 400V into 12V which is fed into the motherboard of the data center. Because of the huge electricity bill for the operating of data centers, very high efficiency AC – DC rectifiers are needed. Existing PFC AC – DC converters use MOSFETs as switching devices. Innovations in MOSFET switches over the last three decades have enabled a steady improvement in power supply design. However, the performance of the MOSFET has reached its theoretical limits. GaN switching technology offers significantly better performance than that of MOSFETs. It grants the opportunity to improve the power density and efficiency of power supplies. A Bridgeless Boost Totem-Pole PFC topology is selected and designed for maximum efficiency by utilizing GaN switches. Merits include: a custom wound inductor to aid in increasing overall efficiency, a detailed PCB layout for the GaN switches and their respective gate drivers, a high and low-side gate resistance analysis for the GaN switches to reduce the rise and fall time of the gate signals while mitigating the Miller Effect, current control of the synchronous MOSFETs to achieve ideal diode emulation, a soft-start function during the zero-crossings of the line voltage to reduce AC current spikes, a fast voltage control loop for better dynamic performance, and two heatsink designs to reduce GaN switch temperatures during high load operation. Mathematical analysis, simulation and hardware testing were done to verify the performance of the design. Start-up performance, dynamic step-load changes and steady-state performance via PF, THD and efficiency recordings over a range of loading conditions.
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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".