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Record W2138414246 · doi:10.1109/pes.2008.4596860

Network-integrated stability enhancement strategy for power system with distributed generations

2008· article· en· W2138414246 on OpenAlexaff
Lin Wang, Helen Cheung, Alexander Hamlyn, R. Cheung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFault (geology)Electric power systemDistributed generationStability (learning theory)Renewable energyComputer scienceLoad SheddingPower (physics)Generator (circuit theory)Network architectureControl engineeringEngineeringDistributed computingElectrical engineeringComputer network

Abstract

fetched live from OpenAlex

This paper proposes a new computer network-integrated stability enhancement strategy for electricity power system connected with renewable-energy distributed generations. The proposed strategy aims to save a distribution system from imminent voltage collapse due to contingencies occurring in the subtransmission network, distribution network, or distributed generation circuit, by executing appropriate operations. The operations include coordinated generator controls, load tap changing, load shedding, etc. 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 the design of a new two-layer architecture for network monitoring, data transmitting, and control delivering. This architecture is fault tolerant and is specially designed for monitoring distribution systems with multiple (over hundred) feeder nodes. Typical adaptive stability enhancement is illustrated.

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)0.000
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 score0.956
Threshold uncertainty score0.549

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 head0.026
GPT teacher head0.211
Teacher spread0.186 · 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.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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