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Record W2544919282 · doi:10.1109/iecon.2008.4757999

A new single phase hybrid passive filter to dampen resonances and compensate harmonics and reactive power under distorted source conditions

2008· article· en· W2544919282 on OpenAlexafffund
S. Rahmani, Ab. Hamadi, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsHarmonicsTotal harmonic distortionPower factorControl theory (sociology)HarmonicAC powerElectronic filterVoltageTopology (electrical circuits)Harmonic analysisFilter (signal processing)Active filterPower (physics)EngineeringAcousticsElectronic engineeringComputer sciencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a new single phase hybrid passive filter (SPHPF) for compensating load voltage and current harmonics, correct power factor, eliminate the chances of series and parallel resonance and eliminates large variation of power factor and terminal voltage with varying loads under stiff and distorted source conditions. The harmonics analysis is performed in order to observe the percentage reduction of total harmonic distortion for passive filter at various loads. The investigations are carried out to validate the performance of proposed SPHPF with detuned resonance, for effective and efficient operation under varying loads and source conditions. The topology of SPHPF 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, which indicates that the hybrid passive filtering performance of this topology is satisfactory. Experimental and simulation results show that the proposed passive filter can simultaneously compensate effectively all voltage and current harmonics and reactive power for large capacity nonlinear load.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.702

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 head0.038
GPT teacher head0.260
Teacher spread0.222 · 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

Citations6
Published2008
Admission routes2
Has abstractyes

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