MétaCan
Menu
Back to cohort
Record W2018815473 · doi:10.1109/pesmg.2013.6672284

A phase-domain synchronous machine model with constant equivalent conductance matrix for EMTP-type solution

2013· article· en· W2018815473 on OpenAlexaff
Liwei Wang, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British ColumbiaABB (Canada)
Fundersnot available
KeywordsEmtpInterfacingReactanceConstant (computer programming)Computer scienceMatrix (chemical analysis)Control theory (sociology)VoltageEngineeringPhysicsArtificial intelligenceElectrical engineeringElectric power systemMaterials science

Abstract

fetched live from OpenAlex

Interfacing machine models in either nodal analysis-based (EMTP-like) or state variable-based transient simulation programs plays an important role in numerical accuracy and computational performance of the overall simulation. As an advantageous alternative to the traditional qd models, a number of advanced phase-domain (PD) and voltage-behind-reactance (VBR) machine models have been recently introduced. However, the rotor-position-dependent conductance matrix in the machine-network interface complicates the use of such models in EMTP. This paper focuses on achieving constant and efficient interfacing circuit for the PD synchronous machine model. It is shown that the machine conductance matrix can be formulated into a constant sub-matrix plus a time-variant sub-matrix. Eliminating numerical saliency from the second term results in a constant conductance matrix of the proposed PD model, which is a very desirable property for the EMTP solution since the re-factorization of the network conductance matrix at every time step is avoided. Case studies demonstrate that the proposed PD model represents a significant improvement over other established models used in EMTP while preserving the accuracy of the original/classical PD model.

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: none
Teacher disagreement score0.895
Threshold uncertainty score0.564

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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicReal-time simulation and control systemsFrench-language works237,207