Three-Phase Buck–Boost Derived PFC Converter for More Electric Aircraft
Bibliographic record
Abstract
In more electric aircraft (MEA), three-phase power factor correction (PFC) rectifiers of several kilowatts are required. In this paper, a three-phase buck-boost derived PFC converter with three switches and with input inductors connected in delta configuration for use in MEA is presented. The proposed converter is operated in a discontinuous conduction mode to achieve PFC at ac input. This avoids the inner current control loop which further eliminates the current sensors. It requires only one output voltage sensor unlike five sensors in conventional PFC converter for its control implementation. This makes the system cost effective, more reliable, and robust. A simple voltage control loop is used to generate the gate signals. The steady state operation of the converter and detailed design calculations are presented. For each power component, the analytical expressions for calculating the average and rms current ratings are derived to facilitate the converter design. The small-signal model of the converter is presented to aid the controller design. The experimental results from a 2 kW laboratory prototype are presented to confirm the operation of the proposed converter. An input power factor of 0.999, an input current total harmonic distortion of as low as 2.76%, and a high conversion efficiency of 96% are achieved from the prototype.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".