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Record W4238864857 · doi:10.1109/tia.2014.2336972

Modeling DC Motor Drive Systems in Power System Dynamic Studies

2014· article· en· W4238864857 on OpenAlexaff
Shengqiang Li, Xiaodong Liang, Wilsun Xu

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of AlbertaPowertech Labs (Canada)
Fundersnot available
KeywordsDC motorTorqueMotor driveEngineeringAutomotive engineeringControl engineeringElectric power systemSystem dynamicsVoltagePower (physics)Computer scienceControl theory (sociology)Control (management)Electrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Direct current (dc) motor drive systems are extensively used in paper, steel, mining, material handling, and other industrial applications due to the high starting torque and easy speed control over a wide range. They could account for 10%-20% load demand in some industrial facilities and thus have significant impact on the overall system dynamics. However, an adequate dynamic model for this type of loads is not available for power system dynamic studies. In this paper, a comprehensive modeling method for dc motor drive systems is proposed considering two scenarios: 1) The drive will trip when subjected to severe voltage sags, and 2) the drive can ride through when experiencing mild voltage sags. The dc drive trip curve and a simple procedure to determine if the drive needs to be included for dynamic studies are proposed for Scenario 1. The dynamic model for dc motor drive systems, which can be readily inserted in the simulation software, is developed and verified through case studies for Scenario 2.

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 score0.977
Threshold uncertainty score0.813

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 integrity0.0000.001
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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