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Record W2120068937 · doi:10.1109/pes.2009.5275187

Network-aided strategy and breaker impacts on voltage instability corrective actions for power systems with DGs

2009· article· en· W2120068937 on OpenAlexaff
Lin Wang, Alexander Hamlyn, Helen Cheung, Rizwan Yasin, Celia Li, Richard Cheung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCircuit breakerElectric power systemFault (geology)EngineeringComputer sciencePower (physics)Load SheddingInstabilityVoltageReliability engineeringControl engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper investigates the impacts of circuit breaker performance on voltage instability corrective actions for power systems with distributed generations (DGs) under contingencies, and proposes a new computer network-aided strategy for stability improvement. This strategy aims to save a distribution system from imminent voltage collapse due to contingencies occurring in subtransmission system, distribution system, or DG systems from renewable energy resources. The proposed strategy is based on an instability corrective action using an optimal load shedding operation and a stability enhancive action using available capacity of DGs. This paper shows the use of state-of-the-art digital signal processing technology for determination of correct stability controls, and the application of modern computer networking technology for monitoring of distribution system operating states and transmitting of data and stability control commands. This paper presents a new two-layer fault-tolerant architecture designed for network-aided voltage instability corrective actions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2009
Admission routes1
Has abstractyes

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