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Record W1854697534 · doi:10.1109/pes.2003.1271028

A new technique for estimation of reactive power demand of a cycloconverter in distributed power generation schemes based on high-speed engines

2004· article· en· W1854697534 on OpenAlexaff
Arman Zarringhalam, Mehrdad Kazerani

Bibliographic record

Venue2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491) · 2004
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCycloconverterAC powerRectifier (neural networks)ThyristorPower (physics)Power factorControl theory (sociology)EngineeringElectronic engineeringVoltageComputer scienceElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

In the distributed power generation based on high-speed engines, the frequency of the voltage generated by the high-speed generator (in the order of thousands of Hz) is much higher than that of the grid (50 or 60 Hz). As a result, a static frequency changer is required for frequency matching as well as output power control. In this paper, a new method for the estimation of the reactive power demand of a naturally commutated cycloconverter (NCC) in a distributed power generation scheme based on high-speed engines has been proposed. As NCC is made up of phase-controlled thyristor converter blocks, it draws reactive power irrespective of the mode of operation (rectifier or inverter) and loading condition. The reactive power demand of the cycloconverter varies with the power delivered to the grid. It is important to compensate dynamically for the reactive power drawn by the NCC using a local static Var compensator. In this paper, the reactive power demand of an NCC has been estimated for a range of output power levels, using the proposed method. The analytical results have been verified using the simulation results obtained from PSCAD/EMTDC simulation package.

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 categoriesMeta-epidemiology (narrow)
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 score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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.

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

Citations2
Published2004
Admission routes1
Has abstractyes

Explore more

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