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Record W2542070683 · doi:10.1109/energy.2008.4781021

Voltage Regulation in Radial Distribution Feeders with High Penetration of Photovoltaic

2008· article· en· W2542070683 on OpenAlexaff
Reinaldo Tonkoski, Luiz A. C. Lopes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsAC powerElectrical engineeringDispatchable generationMicrogridPhotovoltaic systemVoltageOvervoltageResistive touchscreenVoltage optimisationPower factorPenetration (warfare)Voltage regulationPower (physics)EngineeringControl theory (sociology)Automotive engineeringDistributed generationComputer sciencePhysicsRenewable energyControl (management)

Abstract

fetched live from OpenAlex

Overvoltages are one of the main reasons for limiting the amount of active power that can be exported by a microgrid and injected into a low voltage (LV) distribution system. The well-known trade offs used in medium voltage (MV) feeders need to be revisited considering the fact that the impedance of LV feeders is mostly resistive with large R/XLratios. This digests investigates the impact of active power and reactive power variation on the voltage and losses of a radial LV distribution feeder with uniformly distributed loads and non-dispatchable (active power) sources. The feeder characteristics as well as the net active power of the buses are considered in the analysis. This shall give indications on how to decide between PV units with overrated inverters, for additional capacity of reactive power control, or energy storage devices, so as to minimize overvoltages during peak power production.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.149
Teacher spread0.145 · 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

Citations115
Published2008
Admission routes1
Has abstractyes

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