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Record W2615825411 · doi:10.1109/apec.2017.7930612

Design, implementation and analysis of an advanced digital controller for active virtual ground-bridgeless PFC

2017· article· en· W2615825411 on OpenAlexaff
Ken King Man Siu, Yuanbin He, Carl Ngai Man Ho, Henry Shu-Hung Chung, River Tin-Ho Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsController (irrigation)Control theory (sociology)Transient (computer programming)Electromagnetic interferenceWaveformDigital controlEMIComputer scienceOpen-loop controllerInner loopVoltageTransient stateElectronic engineeringEngineeringControl engineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

The paper presents a new digital control scheme for Active Virtual Ground-Bridgeless PFC (AVG-BPFC) in electric vehicle wireless charging application which is able to suppress the common-mode (CM) voltage induced from the conventional BPFC, where the best optimized solution between the system efficiency and Electromagnetic Interference (EMI) performance in the PFC state can be obtained. However, a resonant characteristic will be presented in the current waveform due to the converter form a LCL filter structure. It leads the controller design in the AVG-BPFC to become challenge. Thus, a triple loop control's architecture is proposed where inner loop is designed with boundary controller to eliminate the filter resonance and to control the AC voltage. In the mid-loop, deadbeat controller is applied to maximize the power quality by offering a precise current tracking function. A PI controller is adopted in the outer loop to regulate the output voltage. Such control scheme is implemented digitally on a 1.5 kW prototype. In the paper, theoretical models of the whole system are analyzed and the system performance is successfully verified in both of the steady state and transient state conditions. The experimental results show a good agreement with the theoretical knowledge.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.450

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 communication0.0000.001
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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 designOther design
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

Citations2
Published2017
Admission routes1
Has abstractyes

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