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Record W2086844805 · doi:10.7227/ijeee.37.4.8

Distribution System Voltage Regulation and Var Compensation for Different Static Load Models

2000· article· en· W2086844805 on OpenAlexaff
N. Mithulananthan, M.M.A. Salama, Claudio A. Cañizares, J. Reeve

Bibliographic record

VenueInternational Journal of Electrical Engineering Education · 2000
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl theory (sociology)Voltage regulationConstant power circuitConstant (computer programming)Compensation (psychology)Electrical impedanceConstant currentVoltageShunt (medical)AC powerElectric power systemCapacitorTransmission systemPower (physics)Computer sciencePower factorEngineeringTransmission (telecommunications)Electrical engineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Voltage regulation computations for distribution systems are strongly dependent on power flow solutions. The classical constant power load model is typically used in power flow studies of transmission or distribution systems; however, the actual load of a distribution system cannot just be modeled using constant power models, requiring the use of constant current, constant impedance, exponential or a mixture of all these load models to accurately represent the load. This paper presents a study of voltage regulation of a distribution system using different static load models. The effect of shunt capacitor compensation is also studied and illustrated in this paper for systems with different static load models.

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)0.003
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.220
Teacher spread0.215 · 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

Citations73
Published2000
Admission routes1
Has abstractyes

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