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Record W189903346

Source Node Expansion Algorithm for Coherency Based Islanding of Power Systems

2011· article· en· W189903346 on OpenAlex
Issah Ibrahim

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2011
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsAlgorithmComputer scienceNode (physics)Power (physics)IslandingElectric power systemEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

The electric power system is an exposed man-made structure susceptible to wide arrays of disturbance. If not cleared, a lingering disturbance can plunge the system into the unstable mode in a fairly short time frame. In distributed generation, a drawn-out perturbation can cause system components to operate under unacceptable conditions. When restoration controls fail to revive the troubled system, generators may lose synchronism causing them to swing haphazardly in groups. This crisis separates the power system into unbalanced regions called islands.In this thesis, Source Node Expansion Algorithm based on Slow Coherency has been proposed to resolve unintentional islanding. The algorithm initiates expansion from generator source,engulfs connected loads until desired power mismatch is met. It then terminates and optimal cutsets deduced from the Adjacency Matrix. The proposed technique is tested on 14 and 37-bus systems to endorse its potency. The experimentation is carried out in the PowerWorld platform.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.192
Teacher spread0.170 · 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