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Record W2889500915 · doi:10.1109/itec.2018.8450191

An Implementation of Solar PV Array Based Multifunctional EV Charger

2018· article· en· W2889500915 on OpenAlexaff
Anjeet Verma, Bhim Singh, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsElectrical engineeringPhotovoltaic systemAC powerGridTotal harmonic distortionBattery chargerGrid-connected photovoltaic power systemVoltageBattery (electricity)Solar energyPower (physics)EngineeringComputer scienceMaximum power point trackingPhysics

Abstract

fetched live from OpenAlex

In this paper, a solar PV (Photo-voltaic) array based EV (Electric Vehicle) charger is proposed, which has a bi-directional flow of active and reactive powers. The proposed charger uses a solar PV array energy to charge the EV battery and to feed the grid with the remaining power. In this charger, the VSC (Voltage Source Converter) does the task of harnessing the maximum power from the solar PV array. At the time of high cost of energy, the charger has the provision to inject the battery energy into the grid to earn revenue. In addition to the active power exchange with the grid, the proposed charger also exchanges the reactive power with the grid, simultaneously. In all operating modes, the THD (Total Harmonic Distortion) of the grid current remains within the IEEE 519 standard. The proposed charger is designed for a single phase 230V, 50Hz supply and is experimentally validated in the laboratory.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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 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

Citations75
Published2018
Admission routes1
Has abstractyes

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