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Record W2089351239 · doi:10.1109/pesc.2007.4342425

A New Three Phase Hybrid Passive Filter to Dampen Resonances and Compensate Harmonics and Reactive Power for Any Type of Load under Distorted Source Conditions

2007· article· en· W2089351239 on OpenAlexaff
S. Rahmani, Ab. Hamadi, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHarmonicsHarmonicPower factorControl theory (sociology)AC powerVoltage sourceTopology (electrical circuits)Filter (signal processing)VoltageHarmonic analysisElectronic filterLC circuitPower (physics)EngineeringAcousticsElectronic engineeringComputer sciencePhysicsElectrical engineeringCapacitor

Abstract

fetched live from OpenAlex

This paper proposes a new three phase hybrid passive filter (HPF) for compensating voltage and current harmonics type of load, and power factor. The proposed filter is not sensitive to source impedance variations, eliminate the chances of series and parallel resonance and eliminates large variation of power factor and terminal voltage caused by varying loads under stiff and distorted source conditions. Besides these, the topology also helps fast settling of transients and blocking of harmonics. The investigations are carried out to validate the performance of proposed HPF with detuned resonance, for effective and efficient operation under varying loads and source conditions. The topology of HPF is chosen to shift the resonance frequency of the resulting system below 80 Hz where no excitation is expected. The frequency response shows excellent damping characteristic for load harmonic current above 80 Hz, The simulation results show that the proposed passive filter can simultaneously compensate effectively all harmonics and reactive power for a variety of nonlinear loads.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.525

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 head0.044
GPT teacher head0.314
Teacher spread0.270 · 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 designNot applicable
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
Published2007
Admission routes1
Has abstractyes

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