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Real-Time Hardware Emulation of Microgrid Forming Wireless Power Transfer Systems

2024· article· en· W4403127175 on OpenAlexaff
Ning Lin, Fengqiu Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsEmulationMicrogridComputer scienceWirelessEmbedded systemWireless power transferPower (physics)Computer hardwareOperating system

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.204
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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