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Record W2502641588 · doi:10.1109/tdc.2016.7520089

A novel Newton-Raphson algorithm for power flow analysis in the presence of constant current sources

2016· article· en· W2502641588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsJacobian matrix and determinantCurrent (fluid)AlgorithmConstant currentComputer scienceNewton's methodPower (physics)Electric power systemPower flowPower-flow studyFlow (mathematics)Constant (computer programming)Current loopControl theory (sociology)MathematicsEngineeringElectrical engineeringApplied mathematicsNonlinear systemPhysics

Abstract

fetched live from OpenAlex

This paper presents a version of the Newton-Raphson (NR) algorithm that has been modified to provide a means of facilitating the power flow analysis of loop systems in the presence of current sources that rely on current controlled inverters, such as distributed generators (DGs). The modifications entail the inclusion of the current elements of current sources in the Jacobian matrix of the NR algorithm. The effect is to enable current controlled sources to be modeled directly in the power flow algorithms without the need for converting their currents to the corresponding power components, which is the traditional practice in power flow algorithms. The proposed algorithm has been tested on an IEEE 14-bus system. For loop systems with current controlled sources, the test results show that using this algorithm for power flow analysis offers enhanced accuracy.

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.

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: none
Teacher disagreement score0.949
Threshold uncertainty score0.230

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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