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An Efficient High-power-density Integrated Trap-LCL Filter for Inverters

2023· article· en· W4378842940 on OpenAlexaff
Neda Mazloum, S. Ali Khajehoddin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductanceFilter (signal processing)Trap (plumbing)Electronic engineeringConvertersPower (physics)Electrical engineeringGridInverterTopology (electrical circuits)EngineeringComputer scienceVoltagePhysicsMathematics

Abstract

fetched live from OpenAlex

Trap filters have been proved to increase the power density of the voltage-source converters. Using a trap filter in series with the well-known LCL filters can significantly decrease the required inductance to meet the grid requirements. This paper proposes a new integration method to magnetically couple the trap and converter-side inductances in an LCL structure. The trap winding does not carry the fundamental current component, which further reduces the filter volume and loss. The proposed approach also enables the use of multiple trap filters coupled on one magnetic core. The magnetic and electrical equivalent circuits of the proposed integrated filter are derived and the effectiveness of this method compared with the LCL filter and the conventional integration methods is shown using Ansys Maxwell simulations and experimental results for a 500W/110V grid-connected inverter.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.344

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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