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Record W2168919968 · doi:10.1109/icset.2008.4747157

A model for a fly-wheel driven by a grid connected Switch Reluctance Machine

2008· article· en· W2168919968 on OpenAlexaff
Athula Rajapakshe, Udaya K. Madawala, Dharshana Muthumani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFlywheelVoltage sagGridComputer sciencePower (physics)Switched reluctance motorEngineeringVoltageAutomotive engineeringElectrical engineeringRotor (electric)Power quality

Abstract

fetched live from OpenAlex

This paper presents a PSCAD based simulation model for a grid-connected Switch Reluctance Machine (SRM), which drives a fly-wheel. The system can be used as an effective means to improve power quality issues, such as LVRT, voltage sags, sudden load demands, etc., that encountered in typical power networks. The proposed PSCAD model consists of two separate modules- one for the SRM and the other for the grid side power interface. The SRM module has the flexibility to operate both in motoring and generating modes whereas the power interface module facilitates the bidirectional power flow between the grid and the flywheel in accordance with the mode of operation of the SRM. During standby operation of the flywheel, both modules ensure that the speed of the flywheel is maintained at the desired constant value by operating the SRM machine in the motoring mode. In the event of grid supply failure or voltage sag or sudden load increase, the modules change their mode of operation to generation, during which the flywheel provides its stored kinetic energy back into the grid. Results, for both motoring and generating, are presented as a proof of the validity of proposed simulation module, which can be considered as a valuable tool for investigating grid-connected SRM machines.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.563

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 head0.015
GPT teacher head0.201
Teacher spread0.187 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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