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Record W2615740704 · doi:10.1109/apec.2017.7931149

Performance analysis of grid connected induction motor using floating H-bridge converter

2017· article· en· W2615740704 on OpenAlexaff
Ahteshamul Haque, Siyu Leng, Ian Smith, John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInduction motorPower electronicsCapacitorMotor driveEngineeringElectrical engineeringVoltageConvertersDeratingControl theory (sociology)Computer scienceAutomotive engineering

Abstract

fetched live from OpenAlex

Floating capacitor voltage source power electronic converters can be used as series voltage compensators for grid supplied induction motors to improve their steady-state operating performance, namely: actively reducing the motor power losses over its entire load range; lower the motor's operating temperature to improve lifetime expectancy; the voltage supplied to the motor can be used to avoid derating the motor power rating. These features are especially useful when the motor is connected to a grid whose nominal voltage differs from the machine's rated value or that may fluctuate over time (sag or swell). By injecting a voltage in series with the grid supply, floating capacitor converters can set the motor voltage at a fixed desired value, above or below the grid voltage, under transient or continuous steady-state conditions, and over the entire range of the motor load. These features allow the losses in the motor and power electronics to be controlled, e.g. minimized, hence improve the motor and system efficiencies. For applications where frequency control is not required, the proposed power electronics is more energy efficient; hence a viable cost effective solution. Experimental tests are used to compare the proposed system with several alternatives, using performances such as: power losses and efficiency of the motor, power electronics and the system as a whole.

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: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.492

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 head0.045
GPT teacher head0.252
Teacher spread0.207 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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