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Record W2784326067

Power line communication design and implementation over distribution transformers

2017· article· en· W2784326067 on OpenAlexaff
Seda Üstün Ercan, Okan Özgönenel, Youssef El Haj, C. Christopoulos, David Thomas

Bibliographic record

VenueInternational Conference on Electrical and Electronics Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDistribution transformerPower-line communicationTransformerSmart gridCurrent transformerElectrical engineeringElectric power transmissionMetering modeElectronic engineeringVoltageComputer scienceElectric power distributionDistribution gridEngineeringTransmission lineGridPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

Recently, communication requirements assumed a greater importance following interest in smart grid and cities. This type of communication is simply aimed to facilitate a wide range of applications, i.e. from metering to protection purposes. The traditional power line communication (PLC) has been used for many years over high voltage power lines but there is a need for expanding PLC applications for a more reliable operation of smart grids. For this purpose, this paper seeks to examine PLC application at the medium and low voltage sides over distribution transformers and proposes the transmission line model (TLM) technique to develop high frequency models of power apparatus (line and distribution transformer). The models are then verified by real time experiments on a distribution transformer (50kVA, 34.5/0.4 kV, 50Hz, oil insulated, delta/star connected). Laboratory experiments show that the proposed TLM model is suitable for high frequency modeling of a distribution transformer. Furthermore, the suggested technique is also compared to Matlab simulations.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.569

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 communication0.0000.000
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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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