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Record W2083977250 · doi:10.1109/isie.2006.295759

A Single Phase Multilevel Hybrid Power Filter for Electrified Railway Applications

2006· article· en· W2083977250 on OpenAlexafffund
S. Rahmani, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsRippleHarmonicsActive filterAC powerElectronic engineeringElectronic filterHarmonicControl theory (sociology)Harmonic analysisComputer scienceTraction substationFilter (signal processing)Switched-mode power supplyEngineeringTopology (electrical circuits)VoltageElectrical engineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

To improve the power quality of traction power system, a current control method implemented on a multilevel hybrid power filter (MHPF) to compensate harmonics and reactive power is presented. Regarding the traction substation as a compensating object, the power quality of a traction substation can be improved integrally. The hybrid filter consists of a passive filter and a low-rated multilevel power converter. The passive filter works not only as a harmonic filter tuned at the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> harmonic frequency, but also as a switching-ripple filters. The multilevel active power quality compensator uses source voltage reference to determine the compensating currents for single phase traction power systems. The rating of the switching devices for the active filter decreases with the use a multi-level inverter topology. Simulation results confirm the validity of the system and show that the adopted current control method is able to compensate reactive power and harmonics in the traction substation single phase 25 kV systems

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.631

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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 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

Citations34
Published2006
Admission routes2
Has abstractyes

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