Real-Time Hardware Emulation of Microgrid Forming Wireless Power Transfer Systems
Bibliographic record
Abstract
The prevalence of wireless charging approaches has manifested a grid-supporting potential which can be evaluated by hardware-based methodologies. In this work, real-time emulation of a bidirectional wireless power transfer (WPT) system capable of DC microgrid formation is investigated on the field-programmable gate array (FPGA). Following a detailed analysis of the transfer characteristics of the dual-active bridge with a series-compensated resonant tank in between, a unified control scheme utilizing a phase-shift strategy is proposed for a flexible voltage or current regulation, thereby enabling the DC microgrid-forming capability. Electromagnetic transient modeling is then carried out so that an accurate digital emulation platform is feasible for prototyping. The fact that the WPT has a high frequency compels a small computation step size, which poses a dramatic challenge to real-time execution. A partition-iteration approach is therefore proposed for matrix dimension reduction which ultimately results in an alleviated processing burden. In the meantime, the parallelism of configurable logic blocks and the pipelined architecture of the FPGA are explored to achieve a low hardware latency. The analytical models, as well as the proposed control method, are validated experimentally, and then real-time hardware emulation of a DC microgrid consisting of WPT systems is performed for an integration study.
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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 | 0.000 |
| 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".