MétaCan
Menu
Back to cohort
Record W4214484606 · doi:10.14447/jnmes.v24i3.a04

Real-time Simulations on Ultracapacitor based UPQC for the Power Quality Improvement in the Microgrid

2021· article· en· W4214484606 on OpenAlexvenueno aff
Rahul Wilson Kotla, Srinivasa Rao Yarlagadda

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMicrogridSupercapacitorRenewable energyElectric power systemVoltageMATLABPower (physics)EngineeringAutomotive engineeringAC powerElectrical engineeringControl theory (sociology)Computer scienceCapacitanceControl (management)

Abstract

fetched live from OpenAlex

Penetration of renewable energy systems (RES) into microgrid (MG) increases rapidly due to the intensified energy demands by the distribution level consumers. To meet this demand, consumers are erecting small scale distribution renewable energy generating systems (DREGS) which mostly constitutes of solar photovoltaic systems. Injecting power from the DREGS to the MG will rise potential problems like real and reactive power distortions, sag/swells which affect the power quality of the system. Voltage sags and swells are normally caused by MG intermittencies which occur at the high power and low energy situations. In order to maintain the power quality of the MG during intermittencies, an ultracapacitor (UC) is integrated along with a unified power quality conditioner (UPQC) with the DREGS is proposed in this paper. Basically, an ultracapacitor is a high power and low energy density device that will compensate the MG intermittencies. This proposed system deals with the control and design aspects of the ultracapacitor, a bidirectional converter for charging and discharging of UC, and a UPQC. The UPQC will act as a dynamic voltage restorer (DVR) for the MG side and an active power filter (APF) for the load side. The proposed system is designed and modelled using Matlab/Simulink platform and the results were analyzed.

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)0.001
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 score0.006
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.268
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 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of New Materials for Electrochemical SystemsSame topicElectric and Hybrid Vehicle TechnologiesFrench-language works237,207