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Record W2076767855 · doi:10.1109/pesgm.2014.6939137

Optimal coordinated volt/var control in active distribution networks

2014· article· en· W2076767855 on OpenAlexaff
Maher A. Azzouz, Ehab F. El‐Saadany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAC powerControl theory (sociology)Tap changerVoltageVoltage regulationVoltController (irrigation)Distributed generationComputer scienceMATLABVoltage controllerVoltage optimisationProbabilistic logicEngineeringControl (management)Renewable energyVoltage regulatorVoltage droopElectrical engineering

Abstract

fetched live from OpenAlex

Voltage regulation in active distribution systems (ADN) becomes more challenging due to distributed generation (DG) interference with the conventional voltage control devices. The reasons behind that are the reverse power flow which is caused by the DG units and the probabilistic nature of renewable-based DG units. In this paper, a GA-based voltage regulation schemes is proposed to determine the optimal settings for the DG reactive powers. Moreover, a new hysteresis controller for the on-load tap changer (OLTC) is proposed, which utilizes the system's maximum and minimum voltages, to provide a proper voltage regulation when the OLTC feeds multiple feeders. The proposed control algorithms are coordinated to achieve an effective voltage regulation with less tap operation and DG reactive power. The proposed coordination is tested using a radial structure distribution network which is modeled using Matlab.

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)0.001
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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