Guest editorial: Medium‐ and high‐frequency converters for low‐ and medium‐voltage applications
Bibliographic record
Abstract
Distributed renewable energy resources and the reliance on clean energy have provided a way to meet the ever-increasing global energy demand in an environmentally sustainable manner. In addition, the global net-zero carbon emission target to combat climate change has put pressure on developing new power conversion technologies to integrate zero-emission vehicles and utilise renewable energy resources. On-going research to develop innovative, practical and highly efficient power interfaces, from low-voltage, high-frequency power supplies to medium-voltage, medium-to-high frequency power converters, is essential to facilitate various emerging power conversion systems efficiently, and to satisfy the global energy demand. Next-generation power electronic converters and inverters topologies will inevitably be expected to exhibit much higher power density, higher reliability, increased compactness, higher efficiency and lower cost. With recent advancements in new semiconductor technology, the use of wide-bandgap (WBG) devices, particularly in low and medium voltage power electronic converter systems, is attractive due to the WBG devices’ inherent low on-state resistance, high voltage blocking capability, high thermal performance and high frequency switching characteristic. The emergence of new WBG technology will enable the development of new high-frequency power converters and inverters with a much lower system footprint that will significantly reduce the overall energy conversion system size and weight, with substantial improvement in power density. This special issue aims to foster and document the latest research in power electronic converters and inverters that address the above challenges. Prospective authors are invited to submit original research or review articles in the above areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".