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Record W4255277418 · doi:10.32920/ryerson.14646195

System-Wide Enhancement Of Distribution System Voltage Stability Operations

2021· preprint· en· W4255277418 on OpenAlexaff
Alexander Hamlyn

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl theory (sociology)VoltageTransformerCompensation (psychology)Voltage regulationStability (learning theory)Electric power systemComputer scienceVoltage optimisationControl engineeringEngineeringPower (physics)Reliability engineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

To take on the challenge for improving the distribution system voltage stability, this thesis research carried out an extensive study of the stability issues and available technology dealing with the stability problems. Load shedding, Load reduction through transformer tap changing, reactive power compensation, and DG control are investigated in detail. The new strategies proposed, and formulations presented, in this thesis research, are designed for carrying out the corrective actions against voltage instability with a great degree of feasability to achieve optimal operations. A new concept of composite power was developed in this thesis research, for prediction of the trend of voltage stability, and a novel prediction of voltage stability and consequently determination of corrective actions was formulated. A detailed hardware/software based development of the algorithm and strategy for voltage stability enhancement was presented. A detailed set of case studies for verification of the voltage stability enhancement was developed.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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