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Record W3148841045 · doi:10.1002/9781119710905.ch12

Power Quality Enhancement and Grid Support Using Solar Energy Conversion System

2021· other· en· W3148841045 on OpenAlexaff
CH. S. Balasubrahmanyam, Om Hari Gupta, Vijay K. Sood

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsHarmonicsAC powerPhotovoltaic systemPower factorElectronic engineeringElectric power systemConvertersComputer scienceEngineeringElectrical engineeringPower (physics)Automotive engineeringVoltagePhysics

Abstract

fetched live from OpenAlex

Converter-based loads are increasing due to their numerous advantages such as easy control, modularity and reduced cost. Converters suffer from reactive power consumption and generate harmonics that are injected into the system. Conventionally, passive power filters and capacitor banks take care of harmonics and reactive power respectively. However, they are bulky and possess challenges like series and parallel resonance. The shunt active power filters (APFs) could be considered the resolution for the same. If the shunt APF is operated in distribution-static compensator (D-STATCOM) mode, both i.e. load harmonics and reactive power could be compensated. A solar energy conversion system has been used in this study for the compensation of both load harmonics and load reactive power along with injection of the power generated. MATLAB-based simulations are carried out for the implementation of the same. Results depict the successful operation of the solar energy conversion system with grid support and enhancement of power quality.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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Same topicMicrogrid Control and OptimizationFrench-language works237,207