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Record W2556829609

Generalized Dynamic Phasor Based Simulations for Power Systems

2013· article· en· W2556829609 on OpenAlexaboutno aff
M. A. Kulasza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsnot available
Fundersnot available
KeywordsHarmonicsPhasorElectric power systemPower electronicsConvertersPower (physics)Computer scienceTransmission systemHydroelectricityEngineeringElectronic engineeringElectrical engineeringTransmission (telecommunications)Voltage
DOInot available

Abstract

fetched live from OpenAlex

Wide-spread usage of power electronics has enabled the power systems industry to increase the efficiency of modern power systems [1] as well as incorporate new forms of clean energy generation. For example, the Nelson River dc transmission system allows Manitoba Hydro to efficiently transmit clean hydroelectric energy generated in northern Manitoba to the main population centres in the southern parts of the province [2]. However, power electronic converters and devices contribute to the harmonics present in power systems [3]. Therefore, modern simulation techniques must address the increasing presence of harmonics produced by power electronic devices. The purpose of this project is to investigate the use of dynamic phasors to create a generalized, multi-purpose simulator. The simulator will be capable of carrying out simulations on a user defined system using the desired number of harmonics. Simulations have been carried out for general linear systems and a phase-locked loop model has been developed for testing with the linear circuit simulator. Future work includes adding nonlinear models, such as HVDC converters and synchronous machines, as well as additional control system components.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.232
Teacher spread0.222 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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