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Record W2100877934 · doi:10.1109/tpwrd.2012.2187077

A Practical Solution for the Current and Voltage Fluctuation in Power Systems

2012· article· en· W2100877934 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

VenueIEEE Transactions on Power Delivery · 2012
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsQueen's UniversityRoyal Military College of Canada
FundersUniversity of Sharjah
KeywordsGradient descentVoltageConvergence (economics)Control theory (sociology)Current (fluid)Electric power systemComputer sciencePower (physics)Method of steepest descentElectronic engineeringEngineeringMathematical optimizationMathematicsControl (management)Electrical engineeringArtificial neural networkPhysics

Abstract

fetched live from OpenAlex

This paper presents an innovative formulation and implementation for the steepest descent method to minimize the current and voltage fluctuation in power systems. The steepest decent is selected because of its mathematical simplicity. Its main disadvantage is circumvented by proposing a technique that selects proper initial values in order to guarantee its convergence. This technique depends on the Yule-Walker equation. The suggested technique for disturbance extraction is utilized to operate a power conditioner in order to mitigate the current fluctuation, and its performance is compared with the output of the vector control that is commonly used for the mitigation of the current and voltage fluctuation. The presented ideas are conveyed and approved through simulation results using Malab/Simulink. Moreover, some experimental results are provided to affirm the practicality of the presented concepts.

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 score0.990
Threshold uncertainty score0.455

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 head0.027
GPT teacher head0.265
Teacher spread0.239 · 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