Soft-Switched Step-Up Medium Voltage Power Converters
Bibliographic record
Abstract
With a ten-year average annual growth rate of 19 percent, wind energy has been the largest source of new electricity generation for the past decade. Typically, an offshore wind farm has a medium voltage ac (MVac) grid that collects power from individual wind turbines. Since the output voltage of a wind turbine is too low (i.e., typically 400 690 V) to be connected to the MVac grid (i.e., 20 40 kV), a heavy line-frequency transformer is used to step up the individual turbines output voltage to the MV level. To eliminate the need for bulky MVac transformers, researchers are gravitating towards the idea of replacing the MVac grid with a medium voltage dc (MVdc) grid, so that MV step-up transformers are replaced by MV step-up power electronic converters that operate at the medium frequency range with much lower size and weight. \nThis dissertation proposes a class of modular step-up transformerless MV SiC-based power converters with soft-switching capability for wind energy conversion systems with MVdc grid. This dissertation consists of two parts: the first part focuses on the development of two novel groups of step-up isolated dc-dc MV converters that utilize various step-up resonant circuits and soft-switched high voltage gain rectifier modules. An integrated magnetic design approach is also presented to combine several magnetic components together in the modular high voltage gain rectifiers. The second part of this dissertation focuses on the development of several three-phase ac-dc step-up converters with integrated active power factor correction. In particular, a bridgeless input ac-dc rectifier is also proposed to combine with the devised step-up transformerless dc-dc converters (presented in the first part) to form the three-phase soft-switched ac-dc step-up voltage conversion unit. In each of the presented modular step-up converter configurations, variable frequency control is used to regulate the output dc voltage of each converter module. The operating principles and characteristics of each presented converter are provided in detail. The feasibility and performance of all the power converter concepts presented in this dissertation are verified through simulation results on megawatts (MW) design examples, as well as experimental results on SiC-based laboratory-scale proof-of-concept prototypes.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".