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Digital Implementation of Sliding Mode Control Technique on SSC for Load Balancing and Reactive Power Compensation

2003· article· en· W2053831709 on OpenAlexaff
Bhim Singh, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

VenueIETE Journal of Research · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsÉcole de Technologie SupérieureHôpital Notre-Dame
Fundersnot available
KeywordsControl theory (sociology)AC powerThree-phaseController (irrigation)Power factorInsulated-gate bipolar transistorEngineeringPulse-width modulationVoltageCompensation (psychology)Sliding mode controlComputer scienceElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper presents an implementation of a new control scheme of a 3-phase solid-state compensator (SSC) to compensate reactive power and to balance unbalanced loads. An IGBT- based current controlled PWM voltage source inverter (VSI) with a dc bus capacitor is employed as an SSC. A sliding mode controller (SMC) over the dc bus voltage of the SSC is used to derive reference supply currents in phase with supply voltages. The SMC closed loop controller over dc bus voltage of SSC provides robust control and transient free dynamic response of the system. An indirect current control over the reference and sensed supply currents is realised for generating the gating pulses for the IGBTs of the SSC. Test results of the SSC for the cases of single-phase and 3-phase lagging power-factor loads are presented and discussed in detail to demonstrate the reactive power compensation and loads balancing capabilities of the SSC.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.017
GPT teacher head0.331
Teacher spread0.315 · 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 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

Citations3
Published2003
Admission routes1
Has abstractyes

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