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Record W2907261860 · doi:10.1109/iecon.2018.8591826

Single Switch Open-Circuit Fault Detection for Three-Level NPC Inverter Using Conducted Emissions Signature

2018· article· en· W2907261860 on OpenAlexaff
Ibtissem Abari, Mahmoud Hamouda, Jaleleddine Ben Hadj Slama, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEMIElectromagnetic interferenceInverterSignature (topology)Filter (signal processing)ResistorFault (geology)Computer scienceElectronic engineeringInterference (communication)Energy (signal processing)Fault detection and isolationVoltageEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper proposes a single switch open-circuit fault diagnosis approach for three-phase three-level NPC inverter based on conducted emissions. The proposed approach uses a simple electromagnetic interference (EMI) filter, placed between the DC source and the conversion system, to meter conducted interferences. A time-domain analysis of the voltages across the EMI filter resistors is, firstly, utilized to identify the two pair semiconductors among the two upper or the two lower switches. The Teager energy operator based algorithm is, then, applied. This latter proves the efficiency of this method for the identification of the faulty switch. The effectiveness of this pioneering approach is enhanced by simulation results.

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: none
Teacher disagreement score0.873
Threshold uncertainty score0.987

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)0.0010.000

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 head0.121
GPT teacher head0.281
Teacher spread0.160 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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