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Built-in Circuit Redundancy and Fault-tolerant Operation of a New Multi-Mode Hybrid String Inverter/Rectifier Leg

2021· article· en· W3186529362 on OpenAlexaff
Reza Emamalipour, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
Fundersnot available
KeywordsConvertersRedundancy (engineering)InverterElectrical engineeringFault toleranceRectifier (neural networks)Computer scienceElectronic engineeringVoltageEngineeringPower electronicsReliability engineering

Abstract

fetched live from OpenAlex

With ever-increasing growth of renewable energy and storage systems, the reliability and failure analysis of power electronic converters are becoming extensively crucial. Failure in semiconductor devices is considered to be the main source of the system failures and interruptions in power converters. In this paper, the built-in circuit redundancy and the open circuit fault tolerant operation of a new hybrid 6-switch string inverter/rectifier leg is analyzed. In the proposed circuit, the added diodes and low frequency switches allow the circuit to continue to deliver power to the load in case any switch experiences open circuit fault while enabling the converter to operate in different modes adding extra control in regulating the output voltage and preventing the switching frequency from going far away from the resonant frequency. The processor-in-the-loop (PIL) test results are given on a 1kW, 400VDC/700VDC system to highlight the fault-tolerant operation of the 6-switch based CLLC resonant converter.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.254
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2021
Admission routes1
Has abstractyes

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