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Record W2548513154 · doi:10.1109/ccece.2016.7726788

Power system tracking state estimation based on stochastic fractal search technique under sudden load changing conditions

2016· article· en· W2548513154 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
TopicPower System Optimization and Stability
Canadian institutionsDalhousie University
Fundersnot available
KeywordsParticle swarm optimizationElectric power systemComputer scienceNonlinear systemGenetic algorithmFractalTracking (education)State (computer science)EstimationMathematical optimizationControl theory (sociology)Power (physics)AlgorithmEngineeringArtificial intelligenceMathematicsMachine learning

Abstract

fetched live from OpenAlex

Tracking state estimation gives a fast continuous update on the condition of the power system without modelling of the physical time fluctuating nature of the system. These estimation techniques are used to anticipate conceivable potential and security possibilities. Any enhancement in its estimation ability would enhance the security of the present electric networks. This paper discusses nonlinear power system application with Fractal search algorithm under sudden load changes. The application of the proposed algorithm is illustrated on IEEE 5, 14, 30, and 57 bus systems and the results are compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to show the improvements.

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 score0.988
Threshold uncertainty score0.533

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 head0.245
Teacher spread0.233 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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