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Record W2101434476 · doi:10.1109/tpwrs.2011.2175256

Implementation of a Modified Augmented Nodal Analysis Based Transformer Model into the Backward Forward Sweep Solver

2011· article· en· W2101434476 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

VenueIEEE Transactions on Power Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTransformerNodal analysisSolverGroundElectrical impedanceComputer scienceDistribution transformerThree-phaseAlgorithmVoltageMathematical optimizationElectronic engineeringEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, a general method is presented to handle transformers in the backward forward sweep (BFS) based load flow analysis of unbalanced distribution systems. The proposed method is based on the modified augmented nodal analysis (MANA) approach and simply provides a single matrix that is used for both backward and forward sweep operations. The matrix is constructed in a straightforward and systematic manner, and the proposed solution technique is not restricted to a particular transformer connection and can easily handle uncommon configurations, some of which may present modeling challenges otherwise. The transformer impedances are described in full three-phase ohmic values in the matrix, which is the state of the art in distribution system analysis. Phase shifts introduced by different type of transformer connections are automatically taken into account and neutral grounding impedances are explicitly represented.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.240
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