MétaCan
Menu
Back to cohort
Record W4381335687 · doi:10.24084/repqj21.251

Experimental set-up to study power quality in single-phase split-phase distribution systems

2023· article· en· W4381335687 on OpenAlexaboutno aff
I. Vicente, Amaia Arrinda, J.E. Rodríguez-Seco, Lakshan Piyasinghe

Bibliographic record

VenueRenewable Energy and Power Quality Journal · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
FundersEusko Jaurlaritza
KeywordsMetering modeSoftware deploymentTransformerDistribution transformerVoltageElectric power systemElectric power distributionElectricity meterPower consumptionElectrical engineeringSmart meterPower qualityComputer sciencePower (physics)EngineeringAutomotive engineeringSmart grid

Abstract

fetched live from OpenAlex

Power Quality (PQ) has been an important topic since the creation of distribution systems. The deployment of the Advanced Metering Infrastructure (AMI) provided an important tool to measure the PQ of the electric power in the consumption points. One of the smallest secondary distribution systems in terms of power consumption is the single-phase split-phase system (120 V/240 V) that countries such as the United States, Canada, and some countries of central and south America have. Due to its size, this secondary distribution system is more prone to PQ issues. To that end, an experimental set-up was built by the authors so the distribution system from the Low Voltage (LV) transformer to the final appliances of the different houses was emulated. The aim is to capture the currents and voltages observed by the smart meter located at the entrance of the house and look for the different responses. A combination of real and dummy loads was installed in the set-up, so real noise could also be simulated. The set-up was totally automated by an industrial controller and relays, and it produced a very detailed dataset that could be used for multiple purposes.

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 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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.063
GPT teacher head0.363
Teacher spread0.300 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRenewable Energy and Power Quality JournalSame topicGeophysical and Geoelectrical MethodsFrench-language works237,207