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Record W2163900208 · doi:10.1109/tpwrs.2008.926423

A Voltage-Behind-Reactance Induction Machine Model for the EMTP-Type Solution

2008· article· en· W2163900208 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia
FundersPurdue University
KeywordsEmtpReactanceStatorTransient (computer programming)EngineeringControl theory (sociology)Computer scienceRotor (electric)Electric power systemVoltageControl engineeringElectronic engineeringPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Recently, there has been renewed interest in modeling of electrical machines for the electro-magnetic transient program (EMTP)-type programs, with the goal of improving the machine- network interface. In this paper, we present a new voltage-behind- reactance induction machine model for the EMTP-type solution and power system transients. In the proposed model, the stator circuit is represented in abc phase coordinates and the rotor subsystem is expressed in qd arbitrary reference frame. Similar to the recently proposed synchronous-machine voltage-behind-reactance model and the established phase-domain model, simultaneous solution of the machine-network electrical variables is achieved. Efficient numerical implementation of the proposed model is presented, in which one time-step requires as little as 108 flops, taking 1.6 mus of CPU time. Case studies of induction machine start-up transients demonstrate that the proposed model is more accurate and efficient than several existing EMTP machine models.

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.

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 score0.989
Threshold uncertainty score0.776

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 head0.027
GPT teacher head0.229
Teacher spread0.202 · 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