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Record W4401079428 · doi:10.23977/jeeem.2024.070215

Investigations on the Influence of D-FACTS Equipment on the Stability of Distribution Network

2024· article· en· W4401079428 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Grid and Power Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDistribution (mathematics)Stability (learning theory)BusinessComputer scienceMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Distributed generation (DG) with unreasonable location and capacity will adversely affect the network loss, voltage stability and reliability of the distribution network, resulting in the underutilization of distributed energy such as wind energy and solar energy. In this paper, based on the measured wind turbine output data, the distributed flexible AC transmission system (D-FACTS) is used to optimize the power flow of the distribution network to reduce the limitation of DG position and capacity. The double changes of wind turbine output and load at different times are simulated, and the network loss, voltage level and reliability of the system are analyzed. The verification of IEEE36 distribution system shows that the installation of D-FACTS equipment on the distribution network with a large number of DGs plays a role in reducing network loss, stabilizing system voltage and improving reliability.

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)0.001
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: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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