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Record W2554854773 · doi:10.1002/9781118694190.ch2

Solution Techniques for Electromagnetic Transients in Power Systems

2014· other· en· W2554854773 on OpenAlexaff
Jean Mahseredjian, Ilhan Koçar, Ulas Karaagac

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPower (physics)Electrical engineeringComputer sciencePhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

This chapter targets mainly off-line solution methods and tools. The objective is to provide an overview of off-line simulation tools and methods for the computation and analysis of electromagnetic transient (EMT). The chapter focuses on the most widely recognized and available groups of methods applied in industrial grade computer software packages. The initial application of EMT-type tools was the computation of overvoltages in power systems. The main modules of an EMT-type simulation tool are: graphical user interface (GUI), load-flow solution, steady-state solution, initialization: automatic or manual initial conditions, time-domain solution, and waveforms and outputs. These modules are described in the chapter. The simulation of control system dynamics is fundamental to the study of power system transients. The development of control system solution algorithms based on the block-diagram approach was initially triggered by the modelling of synchronous machine exciter systems.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.006

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations10
Published2014
Admission routes1
Has abstractyes

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