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Single-Phase L-Type Bridge Inverter With Parallel Active Power Filter

2025· article· en· W4412164550 on OpenAlexaff
Haitham Elmasry, Shuang Xu, Shufeng Zhang, S. A. Saleh, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsActive power filterActive filterInverterPower (physics)Three-phaseHalf bridgeFilter (signal processing)Bridge (graph theory)H bridgePhase (matter)AC powerComputer scienceElectrical engineeringElectronic engineeringPhysicsEngineeringVoltage

Abstract

fetched live from OpenAlex

The integration of Distributed Energy Resources (DERs) is increasing, but inverters face challenges like harmonic currents. Traditional solutions like passive filters are bulky and costly. This paper introduces a parallel active power filter (PAPF) designed for single-phase L-type bridge inverter, incorporating an H-bridge circuit and a small LC series branch. The PAPF emulates the behavior of L-type filter by modifying LLCL passive filter. The proposed PAPF maintains the advantage of LLCL filter for eliminating harmonics at the switching frequency and mitigate the harmonics at resonance frequency, by using a pseudo connection instead of a real connection at LLCL structure. The operating principle and parameter design of the proposed circuit have been discussed and analyzed. Comparison results have been made between the proposed active filter and LLCL filter and L-type filter to show the advantages of the proposed active filter. Simulation results in MATLAB/SIMULINK verified the effectiveness of the parallel active filter.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.240
Teacher spread0.221 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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