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Record W2047331221 · doi:10.1109/tec.2014.2367041

A Compensated DFT-Based Phase-Angle Estimation for Fast Motor-Bus Transfer Applications

2014· article· en· W2047331221 on OpenAlexaff
Mohammad Zamani, Mohammad R. Dadash Zadeh, T.S. Sidhu

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2014
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsWestern UniversityOntario Tech UniversityKinectrics (Canada)
Fundersnot available
KeywordsRelaySystem busMATLABComputer scienceControl busCAN busSlack busLocal busTransfer (computing)Motor driveMaximum power transfer theoremFast Fourier transformControl engineeringPower (physics)EngineeringComputer hardwareAC powerPower-flow studyElectrical engineeringVoltageAlgorithm

Abstract

fetched live from OpenAlex

Motor-bus transfer systems are designed to maintain process continuity during the transfer of a motor bus from one power source to an alternate power source. A proper motor-bus transfer system must be designed in such a way that it can operate fast, and prevent damages to the motors and loads connected to the transferred bus. This paper investigates the application challenges associated with the design of a fast motor-bus transfer system. The paper also proposes a compensated discrete-Fourier-transform-based algorithm that enables a fast and reliable bus transfer for power generating plants and industrial facilities. The main advantage of the proposed algorithm is its simple implementation using the commercially available relay technologies. The effectiveness of the proposed algorithm is validated through comprehensive digital simulation of realistic motor-bus transfer systems in the PSCAD/EMTDC and MATLAB software environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.217
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2014
Admission routes1
Has abstractyes

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