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Record W2999091049 · doi:10.1049/iet-gtd.2019.0415

Network partitioning approach for reactive power/voltage control using analytical nodes coupling expressions

2020· article· en· W2999091049 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

VenueIET Generation Transmission & Distribution · 2020
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMemorial University of Newfoundland
FundersState Grid Corporation of ChinaNational Natural Science Foundation of China
KeywordsComputer scienceGenerator (circuit theory)GridMerge (version control)Coupling (piping)Electric power systemPower (physics)AlgorithmParallel computingMathematicsEngineering

Abstract

fetched live from OpenAlex

Power‐grid partitioning is an important prerequisite for power systems security analysis and control. In this study, a novel recursive grid bipartitioning strategy is proposed. First, two analytical expressions are derived from Kirchhoff's equations to represent coupling relationships between nodes. Then, generator nodes under study are divided into two groups by recursively applying the coupling relationships between generator nodes. Finally, the coupling relationships between generator and load nodes are used to merge relevant load nodes to the corresponding groups of generators. The stopping criterion of the grid bipartitioning process is also discussed. The proposed bipartitioning strategy is implemented on the IEEE 118‐bus system and a practical provincial power grid in China to demonstrate its effectiveness.

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 score0.977
Threshold uncertainty score0.847

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 head0.044
GPT teacher head0.252
Teacher spread0.208 · 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