MétaCan
Menu
Back to cohort

Reduction in System Losses and Power Demand by Combination of Optimal Power Flow and Conservation Voltage Reduction Using Smart PV Inverters

2019· article· en· W3003330558 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsWestern University
Fundersnot available
KeywordsVoltage reductionReduction (mathematics)AC powerPower (physics)Energy conservationVoltage optimisationVoltageComputer scienceDemand reductionElectric power systemPower flowPower-flow studyControl theory (sociology)Electronic engineeringPower factorAutomotive engineeringEngineeringElectrical engineeringControl (management)MathematicsPhysics

Abstract

fetched live from OpenAlex

This paper presents a new control methodology of smart PV inverters that combines Optimum Power Flow (OPF) and Conservation Voltage Reduction (CVR) in a single step for reduction of system active power demand as well as active power losses in distribution systems. The methodology has been implemented in two different types of distribution systems simulated in PSS®E. The study also underscores the added benefits pertaining to active power demand reduction, that can be extracted out of PV systems in addition to their primary function of real power 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
Teacher spread0.188 · 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

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicOptimal Power Flow DistributionFrench-language works237,207