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Record W2733309778 · doi:10.1109/tpwrs.2017.2721547

Multiphase Load-Flow Solution and Initialization of Induction Machines

2017· article· en· W2733309778 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 · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInitializationControl theory (sociology)AC powerComputationNewton's methodPower-flow studyFlow (mathematics)Computer scienceInduction motorIterative methodSlip (aerodynamics)TorqueMathematical optimizationVoltageMathematicsMechanicsAlgorithmEngineeringPhysicsNonlinear system

Abstract

fetched live from OpenAlex

This paper presents a new method to model induction machines (IMs) in multiphase load-flow calculations. Fast convergence of the load-flow solution is achieved using an iterative Newton method in the modified-augmented-nodal-analysis formulation. The multiphase modeling approach allows accounting for unbalanced networks. The IM is modeled using either a constraint of electrical power input, mechanical power, or mechanical torque output. The slip of the IM becomes a load-flow variable computed iteratively while the reactive power is not fixed. In addition, the initialization of time-domain simulations using load-flow solution in the computation of electromagnetic transients is demonstrated using balanced and unbalanced network cases. It is shown that a seamless transition between load-flow and time-domain average powers is obtained when the slip of the IM is formulated as a variable in load-flow and its reactive power is not fixed.

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 score0.985
Threshold uncertainty score0.488

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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