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Record W2588748418 · doi:10.1109/ecce.2016.7854995

DC-link current ripple component RMS value estimation considering anti-parallel diode reverse recovery in voltage source inverters

2016· article· en· W2588748418 on OpenAlexafffund
Jing Guo, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
FundersCanada Research Chairs
KeywordsRippleInverterDiodeVoltageControl theory (sociology)Current (fluid)Current sourceElectronic engineeringComputer scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

To estimate the RMS value of the DC-link ripple current more accurately, a method is developed considering the reverse recovery of the anti-parallel diode in voltage source inverters (VSIs). In this paper, using the analysis of the inverter DC-link current and anti-parallel diode reverse recovery, calculations are implemented based on the SPWM technique. According to the calculations and simulation and experimental results, it is shown that the calculated ripple current RMS values are higher and closer to the actual values when the diode reverse recovery is considered. In addition, inverter switching frequency also influences this value. Comparing with the experimental results, the error of the DC-link ripple current RMS value estimated by the proposed method vary under different inverter operating conditions and are between 0.1% and 10.5%.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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.

Study designSimulation or modeling
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

Citations1
Published2016
Admission routes2
Has abstractyes

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