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A Study on 3-phase Synchronous Machine Parameters Representations by Various Assumptions

2023· article· en· W4386066502 on OpenAlexaff
In Kwon Park, Gilsoo Jang, Yi Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsRTDS Technologies (Canada)
Fundersnot available
KeywordsInterconnectionComputer scienceGridRepresentation (politics)Renewable energyIndustrial engineeringControl engineeringElectrical engineeringTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

One consistent trend penetrating the development and substantial change introduced in the last couple of decades is the proliferation of the renewables such as Wind and Solar among the traditional grid systems. At the early stage of the introduction, the portion was close to negligible, and the technology associated with those renewable generation systems was beginning to develop. As more of those renewable generation systems are coming with the necessary maturity, the interconnection requirements are changing in the direction of reflecting the realities of the systems. One idea behind the interconnection requirement is how to represent renewable generation systems in an AC grid. Many possible ways were presented, but from the viewpoint of the traditional AC grid engineers, closer proximity to the traditional generation systems, i.e., 3-phase synchronous machine representation, has been desired. However, even for a seasoned generation station engineer, an intuitive understanding of 3-phase synchronous machine model representation has been a high mountain, challenging to climb, if not insurmountable. This paper presents two points in that regard. One is the way to interpret the usual 3-phase synchronous machine parameters (i.e., operational/standard parameters) in a fundamental domain, where the intuitive circuit element type meaning can be conveyed. The second is to derive a more accurate way of interpreting the operational parameters, which was left as future work in a classic textbook.

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 score0.373
Threshold uncertainty score0.684

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)0.0000.001

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 head0.027
GPT teacher head0.311
Teacher spread0.284 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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