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Record W2791742762 · doi:10.1049/iet-pel.2017.0669

Mitigation of the low‐frequency neutral‐point current for three‐level T‐type inverters in three‐phase four‐wire systems

2018· article· en· W2791742762 on OpenAlexaff
Wenping Zhang, Chen Ding

Bibliographic record

VenueIET Power Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsCurrent (fluid)Control theory (sociology)InverterCapacitorPoint (geometry)Power (physics)Low frequencyThree-phasePhase (matter)VoltageReliability (semiconductor)Electrical engineeringComputer scienceEngineeringControl (management)MathematicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Large electrolytic capacitors are normally applied to maintain a stiff DC‐bus in uninterrupted power supply systems. However, the low‐frequency currents flow through them, which can reduce their lifespan and risk the system reliability. Therefore, this study investigates the neutral‐point current and corresponding suppression scenarios for three‐phase four‐wire three‐level T‐type inverters. First, the neutral‐point current for three‐level T‐type inverters is analysed and the mathematical expression is obtained. With the mathematical model, the neutral‐point currents in cases of different load conditions are investigated. In order to reduce the neutral‐point current and extend the lifespan of DC‐bus capacitors, a neutral‐point current suppression control strategy is proposed. The basic concept of the proposed control strategy and its effectiveness in cases of different load conditions are presented. Finally, a 30 kW T‐type three‐level inverter platform is built and the experimental results are presented to verify the theoretical analysis.

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 score0.002

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 science0.0000.000
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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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