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A TPFW Solar PV-BSS-DG set Based Microgrid with Power Quality Improvement and Maximum Power Extraction Capability

2020· article· en· W3147624098 on OpenAlexaff
Bhim Singh, Vivek Narayanan, Seema Seema, Aditya Chandra, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMicrogridPhotovoltaic systemPower factorHarmonicsAC powerDiesel generatorMaximum power point trackingCompensation (psychology)Distributed generationElectrical engineeringVoltageEngineeringComputer scienceControl theory (sociology)Electronic engineeringAutomotive engineeringRenewable energyInverterDiesel fuel

Abstract

fetched live from OpenAlex

In this paper, a three-phase four-wire (TPFW) standalone microgrid is presented for powering the electrical energy to the rural areas where the electric grid is not available. This microgrid is designed with a synchronous generator (SG) based diesel generator (DG) set, a photovoltaic (PV) array and a battery storage system (BSS). The BSS handles the intermittent variation of the PV array power as well as provides power to the load during the heavy load demand. The DG set feeds power to the nonlinear and unbalanced loads. Hence the voltage source converter (VSC) is controlled in such a manner that it provides the harmonics, reactive power as well as the compensation of unbalanced load. Thereby the power quality (PQ) at the DG set is improved. The main functional features of the microgrid, are extraction of maximum PV array output power, DG set terminal voltage regulation, unity power factor (UPF) operation of the DG set by compensating the reactive power, harmonics elimination, compensation of the unbalanced load and neutral current compensation etc.

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: Empirical
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)0.0020.001

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 head0.019
GPT teacher head0.269
Teacher spread0.251 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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