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Record W2908142041 · doi:10.1109/iecon.2018.8592692

Sliding Mode Control of Three-Phase series Hybrid Power Filter with Reduced cost and Rating

2018· article· en· W2908142041 on OpenAlexaff
Muftah Abuzied, Abdelhamid Hamadi, Auguste Ndtoungou, S. Rahmani, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)AC powerPower factorHarmonicsActive filterVoltage sagHarmonicVoltage optimisationEngineeringSwitched-mode power supplyElectronic engineeringElectronic filterComputer scienceVoltageElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents and discusses the viability of 3-phase hybrid series power filter compensator in a realistic distribution network. The proposed configuration can challenge unified power quality conditioner (UPQC) with less components and a cost. It ensures power quality by compensating current harmonics, reactive power, sag voltage, swell voltage and unbalance voltage. The main contribution is the proposed configuration integrating a control algorithm of the series active filter to compensate reactive power rather than using a thyristor controlled reactor (TCR). The strategy consists of integration a current source angle in the reference voltage to shifts a reference voltage to get unity power factor in the grid side. The sliding mode control (SMC) which a powerful control ensures a stability using Lyapunov candidate function validation. In order to reduce the rating of the active filter, a design of the passive filter is integrated to take care of reactive power and current harmonic compensation due to its low impedance at the harmonic frequency. The studies include simulation results and evaluations of the passive components used to compensate current harmonics. For the validation, an implementation and testing results are carry out using Matlab/Power Systems (PS).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.358

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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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