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Current Measurement Using Noninvasive sensors in Mobile MV Substations: Modeling and Simulation

2019· article· en· W2999858077 on OpenAlexaff
Prasad Shrawane, T.S. Sidhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsElectrical conductorGroundTransformerCurrent transformerElectrical engineeringCurrent sensorEngineeringElectric power systemIsolation transformerVoltageElectronic engineeringElectrical equipmentComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Continuous monitoring and measurement of current is an essential part of mobile power generation and distribution systems for protection and control. A noninvasive current sensing device can be easily installed near the main bus and three phase conductors before and after the power transformer. Traditional window-type current transformers need electrical isolation and grounding of the system for installation, maintenance and repair. A noninvasive design of the current transformers reduces all risks and electrical hazards in electrical isolation procedure and decrease the frequency and time of outage. This type of design gives more benefits in case of mobile substations of medium and high voltage where space constraint controls the equipment footprint. For application of such noninvasive devices, it is important to study and analyze the dynamics of magnetic fields surrounding the phase conductors where these current sensors are to be installed. This paper discusses the modeling and simulation analysis towards finding the best location for installation of noninvasive current sensors near a three-phase three-conductors medium voltage power system.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.375

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 head0.039
GPT teacher head0.274
Teacher spread0.235 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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