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Record W2571477019 · doi:10.1109/tdc-la.2016.7805688

Application of a photovoltaic generator to mitigate steel mill voltage fluctuation problems

2016· article· en· W2571477019 on OpenAlexfundno aff
N. Garcia, Miguel Ángel Vargas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
FundersOntario Council on Graduate Studies, Council of Ontario Universities
KeywordsPhotovoltaic systemGenerator (circuit theory)VoltageAutomotive engineeringElectrical engineeringAC powerElectricity generationEngineeringMillPower (physics)Environmental scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents results of a thorough power flow analysis of a steel mill plant in combination with a photovoltaic installation. The photovoltaic generator is used to reduce voltage variations caused by arc furnaces loads used in the steel mill installation. Site measurements of power drawn by a steel facility are fed to the PSS/E simulation software, which is used to evaluate the steady-state voltage profile and power losses. The study on a 230 kV network with highly intermittent steel mill loads shows that voltage regulation is possible as long as the power capacity of the photovoltaic generator is sufficient to meet the requirements of reactive power demands in the network area of interest. With the integration of the photovoltaic generator, voltage deviation index decreased to 0.089% and 0.0015% for scenario of 200 MW and 400 MW of nominal capacity of photovoltaic generation.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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