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Record W1575793933 · doi:10.1109/ccece.2002.1015178

Particle swarm optimizer for constrained economic dispatch with prohibited operating zones

2003· article· en· W1575793933 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
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEconomic dispatchDisjoint setsMathematical optimizationParticle swarm optimizationOperating costComputer scienceMultiplier (economics)Regular polygonArtificial neural networkEngineeringMathematicsElectric power systemArtificial intelligencePower (physics)Economics

Abstract

fetched live from OpenAlex

Practically, not all the operating zones of generation units are available always for load allocation due to some physical operation limitations. Accordingly, these prohibited zones divide the operating region between the minimum and the maximum generation limits into disjoint convex subsets. Units with prohibited operating zones transform the ordinary economic dispatch to a nonconvex optimization problem where the conventional Lagrangian multiplier based methods cannot be directly applied. The paper introduces the particle swarm optimizer (PSO) for solving this nonconvex economic dispatch problem. A 15-unit system with 4 units having prohibited operating zones is used for the application. The results are compared with those obtained by both conventional methods and the Hopfield neural network.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.495

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 head0.222
Teacher spread0.210 · 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

Citations38
Published2003
Admission routes1
Has abstractyes

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