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Record W2785348354 · doi:10.1155/2018/5126404

Fault-Tolerant Control Strategy for Neutral-Point-Clamped Three-Level Inverter

2018· article· en· W2785348354 on OpenAlexfundno aff
Yanxia Shen, Miao Beibei, Dinghui Wu, Kader Ali Ibrahim

Bibliographic record

VenueJournal of Control Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsnot available
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsGovernment of Jiangsu ProvinceNational Natural Science Foundation of ChinaCanadian Celiac Association
KeywordsInverterControl theory (sociology)VoltageFault (geology)Topology (electrical circuits)Pulse-width modulationFault tolerancePoint (geometry)CapacitorPower (physics)Modulation (music)Computer scienceEngineeringControl (management)Electrical engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

A fault-tolerant control technique is discussed for the Neutral-Point-Clamped (NPC) three-level inverter, which ensures that the NPC inverter operates normally even under device failures. A two-level leg is added to the NPC inverter; when the device open circuit fault occurs, the load of this faulty phase is connected to the neutral point of this two-level leg through the bidirectional thyristors. An improved Space Vector Pulse Width Modulation (SVPWM) strategy called “addition and subtraction substitution SVPWM” is proposed to effectively suppress fluctuation in capacitor neutral-point voltages by readjusting the sequence and action time of voltage vectors. The fault-tolerant topology in this paper has the advantages of fewer switching devices and lower circuit costs. Experimental results show that the proposed fault-tolerant system can operate in balance of capacitor neutral-point voltages at full output power and the reliability of the inverter is greatly enhanced.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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