Maximum Boost Control of Diode-Assisted Buck–Boost Voltage-Source Inverter With Minimum Switching Frequency
Bibliographic record
Abstract
Diode-assisted buck-boost voltage-source inverter achieves high voltage gain by introducing a switch-capacitor based high step-up dc-dc circuit between the dc source and inverter bridge. As for the unique structure, various pulse width modulation (PWM) strategies are developed with regard to the chopped intermediate dc-link voltage. In order to maximize voltage gain and increase efficiency, this paper proposes a novel PWM strategy. It regulates the average value of intermediate dc-link voltage in one switching time period (Ts) the same as the instantaneous maximum value of three-phase line voltage by controlling the front boost circuit. Then, the equivalent switching frequency of power devices in the inverter bridge can be reduced to 1/3fs(fs= 1/Ts). The operating principle and closed-loop controller design are analyzed and verified by simulations and experiments. Compared with existing PWM strategies, the new control strategy demonstrates less power device requirement and higher efficiency in high voltage gain applications. It is a more competitive topology for wide range dc/ac voltage regulation in renewable energy applications. Furthermore, with new control strategy, the dc-side inductor current and capacitor voltage contains six-time line-frequency ripples. To overcome the undesired influence of low frequency ripples, it is also suitable for 400-800 Hz medium frequency aircraft and vessel power supply system.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".