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Record W2907713664 · doi:10.1109/naps.2018.8600562

A Network-cognizant Aggregate-frequency Reduced-order Power System Dynamical Model

2018· article· en· W2907713664 on OpenAlexaff
Bo Chen, Abdullah Al-Digs, Yu Christine Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)WeightingFrequency domainAggregate (composite)Generator (circuit theory)Inertial frame of referenceElectric power systemComputer scienceSystem dynamicsPower (physics)Frequency responseTime domainEngineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents a reduced second-order power-system dynamical model that accounts for locational effects of load disturbances on system frequency dynamics over time scales corresponding to inertial and primary-frequency response. The locational aspects are retained in the proposed model by incorporating linearized power-flow balance into differential equations that describe synchronous-generator dynamics. Individual synchronous-generator speed dynamics are then combined into a single aggregate frequency state via weighting factors that can be tuned to maximize the accuracy of the reduced-order model. The proposed reduced-order model is general in the sense that its parameters are related to those of the original full-order model in analytical closed form, so that it can be constructed easily for different systems. Time-domain simulations demonstrate the accuracy of the reduced-order model with various choices of weighting factors and highlight the effect of load disturbance location on aggregate-frequency dynamics.

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 score0.960
Threshold uncertainty score0.769

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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